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2e801aa0-d823-4b8f-96ca-ab90de8fc577	matt-multimodal-attention-level-estimation	2301.09174	null	https://arxiv.org/abs/2301.09174v1	https://arxiv.org/pdf/2301.09174v1.pdf	MATT: Multimodal Attention Level Estimation for e-learning Platforms	This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals obtained from the behavior and physiological processes that have been related to modeling cognitive load such as faces gestures (e.g., blink rate, facial actions units) and user actions (e.g., head pose, distance to the camera). The multimodal system uses the following modules based on Convolutional Neural Networks (CNNs): Eye blink detection, head pose estimation, facial landmark detection, and facial expression features. First, we individually evaluate the proposed modules in the task of estimating the student's attention level captured during online e-learning sessions. For that we trained binary classifiers (high or low attention) based on Support Vector Machines (SVM) for each module. Secondly, we find out to what extent multimodal score level fusion improves the attention level estimation. The mEBAL database is used in the experimental framework, a public multi-modal database for attention level estimation obtained in an e-learning environment that contains data from 38 users while conducting several e-learning tasks of variable difficulty (creating changes in student cognitive loads).	['Javier Ortega-Garcia', 'Ruth Cobos', 'Ruben Tolosana', 'Julian Fierrez', 'Aythami Morales', 'Luis F. Gomez', 'Roberto Daza']	2023-01-22	null	null	null	null	['head-pose-estimation', 'facial-landmark-detection']	['computer-vision', 'computer-vision']	[-1.36071905e-01 1.77356660e-01 8.73400830e-03 -3.22421134e-01 -5.96907258e-01 -3.14686149e-01 1.23920359e-01 2.32843578e-01 -5.07369041e-01 4.39210534e-01 1.49817899e-01 3.23162556e-01 -1.31687909e-01 -2.71623820e-01 -4.46122110e-01 -7.36727178e-01 9.46587548e-02 -1.07519761e-01 -2.03562170e-01 -1.68885231e-01 7.23981202e-01 9.32861626e-01 -2.45923066e+00 3.93126845e-01 6.02628946e-01 1.12860835e+00 -1.25129879e-01 1.08296549e+00 1.19365908e-01 8.07803273e-01 -7.47200668e-01 -2.87333041e-01 -5.61539769e-01 -2.38557398e-01 -6.92199051e-01 2.06946377e-02 9.11656439e-01 -3.24931324e-01 -1.26538292e-01 8.91978621e-01 1.12831688e+00 3.88563305e-01 5.13660371e-01 -1.54546273e+00 -1.37582093e-01 6.33553937e-02 -2.57511735e-01 4.98095304e-01 8.68260086e-01 1.35791332e-01 3.17797631e-01 -1.03395212e+00 3.96607891e-02 1.49935293e+00 3.10706437e-01 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1.13419130e-01 -1.53461349e+00 -5.00667393e-01 3.72757941e-01 1.05156690e-01 -8.92520189e-01 -4.62511480e-01 1.09770678e-01]	[13.506632804870605, 2.3923935890197754]
46616c8b-bd08-43ee-81e8-8b14fd538a24	a-comprehensive-study-of-batch-construction	1705.02414	null	http://arxiv.org/abs/1705.02414v1	http://arxiv.org/pdf/1705.02414v1.pdf	A comprehensive study of batch construction strategies for recurrent neural networks in MXNet	In this work we compare different batch construction methods for mini-batch training of recurrent neural networks. While popular implementations like TensorFlow and MXNet suggest a bucketing approach to improve the parallelization capabilities of the recurrent training process, we propose a simple ordering strategy that arranges the training sequences in a stochastic alternatingly sorted way. We compare our method to sequence bucketing as well as various other batch construction strategies on the CHiME-4 noisy speech recognition corpus. The experiments show that our alternated sorting approach is able to compete both in training time and recognition performance while being conceptually simpler to implement.	['Hermann Ney', 'Pavel Golik', 'Patrick Doetsch']	2017-05-05	null	null	null	null	['noisy-speech-recognition']	['speech']	[ 7.33895227e-02 -1.12162121e-01 -7.97594115e-02 -8.25318336e-01 -2.19109848e-01 -3.86758238e-01 6.05795622e-01 -3.47109139e-01 -9.36227083e-01 6.26857102e-01 1.97886512e-01 -1.09693849e+00 3.17131132e-02 -4.84426111e-01 -3.70520949e-01 -7.52295554e-01 -3.41583081e-02 6.86424911e-01 3.20139050e-01 -2.09808603e-01 3.26605856e-01 7.46796370e-01 -1.68625331e+00 6.42319739e-01 3.38315554e-02 7.78047562e-01 2.81252027e-01 1.00233233e+00 -4.85329717e-01 1.06688809e+00 -7.59233236e-01 -8.94605443e-02 2.65345007e-01 -4.11128283e-01 -1.10169184e+00 1.31766573e-01 3.55944097e-01 -2.05396295e-01 -3.55259567e-01 6.66329503e-01 6.68627739e-01 4.28960234e-01 1.04238018e-01 -7.63419986e-01 2.42285982e-01 1.16766167e+00 -5.01073822e-02 7.29890049e-01 3.52991112e-02 -1.49028793e-01 9.54829812e-01 -8.28501403e-01 7.02382505e-01 1.00986052e+00 7.20967114e-01 7.63007998e-01 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-1.05667198e+00 -5.23281157e-01 -9.89410281e-02 3.89071077e-01 -1.70687005e-01 -2.53529102e-01 -3.45171615e-02]	[10.903983116149902, 6.394798755645752]
34addedd-1b3f-498a-a63d-a5c603f3fca8	video-representation-learning-with-visual	2006.15489	null	https://arxiv.org/abs/2006.15489v2	https://arxiv.org/pdf/2006.15489v2.pdf	Video Representation Learning with Visual Tempo Consistency	Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition. In this work, we demonstrate that visual tempo can also serve as a self-supervision signal for video representation learning. We propose to maximize the mutual information between representations of slow and fast videos via hierarchical contrastive learning (VTHCL). Specifically, by sampling the same instance at slow and fast frame rates respectively, we can obtain slow and fast video frames which share the same semantics but contain different visual tempos. Video representations learned from VTHCL achieve the competitive performances under the self-supervision evaluation protocol for action recognition on UCF-101 (82.1\%) and HMDB-51 (49.2\%). Moreover, comprehensive experiments suggest that the learned representations are generalized well to other downstream tasks including action detection on AVA and action anticipation on Epic-Kitchen. Finally, we propose Instance Correspondence Map (ICM) to visualize the shared semantics captured by contrastive learning.	['Bolei Zhou', 'Ceyuan Yang', 'Yinghao Xu', 'Bo Dai']	2020-06-28	null	null	null	null	['action-anticipation']	['computer-vision']	[ 3.79870057e-01 -1.53366506e-01 -7.67902434e-01 -4.96735901e-01 -5.93026221e-01 -2.85667211e-01 6.78701639e-01 -7.65672252e-02 -2.38692909e-01 4.45564598e-01 6.70909464e-01 3.65869433e-01 5.84757794e-03 -4.27400708e-01 -7.03663528e-01 -7.24197388e-01 -4.40044880e-01 -1.35103511e-02 1.58121243e-01 -1.35605752e-01 2.09148988e-01 -4.83157784e-02 -1.76727331e+00 1.14612448e+00 3.66450101e-01 9.37351167e-01 2.96978354e-01 7.63122201e-01 1.31860286e-01 1.62059426e+00 -5.13010740e-01 -6.89446344e-04 1.55359626e-01 -7.61095881e-01 -9.90001559e-01 2.65877992e-01 4.51149970e-01 -3.68437022e-01 -8.38456035e-01 8.25219810e-01 2.01039657e-01 4.73096788e-01 6.39263034e-01 -1.52498710e+00 -5.83534896e-01 6.67144477e-01 -7.77546108e-01 8.09190750e-01 6.34129226e-01 4.52131033e-01 1.18089020e+00 -6.01969302e-01 9.64412630e-01 1.32729685e+00 2.71051794e-01 5.99358380e-01 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9.73044693e-01 1.85240909e-01 -9.22937691e-01 -3.52400005e-01 5.98733187e-01]	[8.601305961608887, 0.7505678534507751]
68472d2c-da46-4bd1-917f-e0405033bbd9	frenlys-a-tool-for-the-automatic	null	null	https://aclanthology.org/2021.ranlp-main.135	https://aclanthology.org/2021.ranlp-main.135.pdf	FrenLyS: A Tool for the Automatic Simplification of French General Language Texts	Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents. In this paper, we present the first automatic LS service for French, FrenLys, which offers different techniques to generate, select and rank substitutes. The paper describes the different methods proposed by our tool, which includes both classical approaches (e.g. generation of candidates from lexical resources, frequency filter, etc.) and more innovative approaches such as the exploitation of CamemBERT, a model for French based on the RoBERTa architecture. To evaluate the different methods, a new evaluation dataset for French is introduced.	['Thomas François', 'Patrick Watrin', 'Quentin Langlois', 'Eva Rolin']	null	null	https://aclanthology.org/2021.ranlp-1.135	https://aclanthology.org/2021.ranlp-1.135.pdf	ranlp-2021-9	['lexical-simplification']	['natural-language-processing']	[-1.64917275e-01 3.25402766e-01 2.00971738e-01 -3.36149007e-01 -7.24006951e-01 -8.04954946e-01 8.79299998e-01 4.96674389e-01 -6.95876598e-01 1.22161210e+00 6.04299724e-01 -1.28975376e-01 -2.59264857e-01 -7.45667100e-01 -2.92630136e-01 4.58441041e-02 7.28702068e-01 8.86188626e-01 2.98521459e-01 -8.26719820e-01 2.92399496e-01 6.40588820e-01 -1.67801964e+00 6.57726228e-01 1.20008636e+00 2.53794342e-01 3.84559840e-01 3.98876905e-01 -4.44563329e-01 3.47800910e-01 -1.01353228e+00 -7.90902317e-01 6.10094443e-02 -6.31970406e-01 -8.86101067e-01 -3.80102813e-01 2.40693942e-01 2.63525933e-01 2.46386856e-01 1.00758958e+00 4.64999110e-01 3.46939743e-01 7.50835001e-01 -3.93770665e-01 -3.03663999e-01 1.40912712e+00 4.54234749e-01 9.43377987e-02 1.15382290e+00 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e321f44d-f6f0-4597-9869-b0a1e89aa9d6	foundations-and-modelling-of-dynamic-networks	2005.07496	null	https://arxiv.org/abs/2005.07496v2	https://arxiv.org/pdf/2005.07496v2.pdf	Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey	Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology	['Bogdan Gabrys', 'Katarzyna Musial', 'Joakim Skarding']	2020-05-13	null	null	null	null	['dynamic-link-prediction']	['graphs']	[ 1.08528554e-01 1.98004901e-01 -5.83483160e-01 -9.93032530e-02 8.07809591e-01 -6.25592172e-01 4.65888590e-01 3.15194935e-01 1.19418152e-01 4.01009321e-01 1.32536404e-02 -6.97477877e-01 -9.19971645e-01 -1.10699618e+00 -9.93601829e-02 -3.88042539e-01 -7.33090281e-01 2.12020054e-01 1.55890018e-01 -3.96788001e-01 -6.00700974e-02 6.35738671e-01 -1.14257252e+00 -3.13957423e-01 5.78790426e-01 6.08360767e-01 -3.28872472e-01 4.88704979e-01 -2.38904998e-01 8.90556455e-01 -4.68647212e-01 -6.97171330e-01 1.09802410e-01 -3.48407716e-01 -7.10036755e-01 -3.09122145e-01 1.29293099e-01 1.63435206e-01 -1.06037867e+00 1.02064478e+00 3.25585246e-01 2.32045636e-01 3.97177905e-01 -1.83199728e+00 -9.19556439e-01 8.51172209e-01 -2.73028284e-01 5.19673347e-01 1.87058032e-01 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3bf4f837-45ba-4b0b-ba90-185755ac9dca	temporal-view-synthesis-of-dynamic-scenes	2208.09463	null	https://arxiv.org/abs/2208.09463v1	https://arxiv.org/pdf/2208.09463v1.pdf	Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images	The challenge of graphically rendering high frame-rate videos on low compute devices can be addressed through periodic prediction of future frames to enhance the user experience in virtual reality applications. This is studied through the problem of temporal view synthesis (TVS), where the goal is to predict the next frames of a video given the previous frames and the head poses of the previous and the next frames. In this work, we consider the TVS of dynamic scenes in which both the user and objects are moving. We design a framework that decouples the motion into user and object motion to effectively use the available user motion while predicting the next frames. We predict the motion of objects by isolating and estimating the 3D object motion in the past frames and then extrapolating it. We employ multi-plane images (MPI) as a 3D representation of the scenes and model the object motion as the 3D displacement between the corresponding points in the MPI representation. In order to handle the sparsity in MPIs while estimating the motion, we incorporate partial convolutions and masked correlation layers to estimate corresponding points. The predicted object motion is then integrated with the given user or camera motion to generate the next frame. Using a disocclusion infilling module, we synthesize the regions uncovered due to the camera and object motion. We develop a new synthetic dataset for TVS of dynamic scenes consisting of 800 videos at full HD resolution. We show through experiments on our dataset and the MPI Sintel dataset that our model outperforms all the competing methods in the literature.	['Rajiv Soundararajan', 'Pranali Sancheti', 'Nagabhushan Somraj']	2022-08-19	null	null	null	null	['video-prediction']	['computer-vision']	[ 3.00022304e-01 -5.12401573e-02 2.44791895e-01 -1.47287220e-01 -3.45306188e-01 -3.70756656e-01 6.39957726e-01 -6.73145056e-01 -8.33088458e-02 5.57761550e-01 3.92754763e-01 1.13220491e-01 2.38913864e-01 -6.92513764e-01 -9.29061353e-01 -6.83078945e-01 -6.55109212e-02 1.68702692e-01 6.42891884e-01 -3.93019430e-02 7.90076852e-02 6.75252676e-01 -1.85712826e+00 8.57576966e-01 3.32228303e-01 9.78111565e-01 5.87971389e-01 1.20445788e+00 1.62149742e-01 9.75962579e-01 -3.69311929e-01 -9.48289875e-03 4.63256925e-01 -3.76588285e-01 -6.82341516e-01 5.32706320e-01 6.46326721e-01 -9.71713722e-01 -6.35531545e-01 5.02888262e-01 3.62295926e-01 2.22156361e-01 1.49327293e-01 -1.04110563e+00 1.28134623e-01 -9.14015248e-02 -6.71483278e-01 2.64999121e-01 7.77737856e-01 2.42435545e-01 4.88465965e-01 -1.28088999e+00 1.32159865e+00 1.33102381e+00 4.24749255e-01 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a438af81-5131-4e6c-9979-6ac8d5aaae4b	taking-a-step-back-with-kcal-multi-class	2202.07679	null	https://arxiv.org/abs/2202.07679v3	https://arxiv.org/pdf/2202.07679v3.pdf	Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks	Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. In high-risk applications like healthcare, practitioners require $\textit{fully calibrated}$ probability predictions for decision-making. That is, conditioned on the prediction $\textit{vector}$, $\textit{every}$ class' probability should be close to the predicted value. Most existing calibration methods either lack theoretical guarantees for producing calibrated outputs, reduce classification accuracy in the process, or only calibrate the predicted class. This paper proposes a new Kernel-based calibration method called KCal. Unlike existing calibration procedures, KCal does not operate directly on the logits or softmax outputs of the DNN. Instead, KCal learns a metric space on the penultimate-layer latent embedding and generates predictions using kernel density estimates on a calibration set. We first analyze KCal theoretically, showing that it enjoys a provable $\textit{full}$ calibration guarantee. Then, through extensive experiments across a variety of datasets, we show that KCal consistently outperforms baselines as measured by the calibration error and by proper scoring rules like the Brier Score.	['Jimeng Sun', 'Shubhendu Trivedi', 'Zhen Lin']	2022-02-15	null	null	null	null	['supervised-dimensionality-reduction', 'network-embedding']	['computer-vision', 'methodology']	[ 4.16343473e-02 5.71115434e-01 -5.66412389e-01 -9.41910744e-01 -1.09948003e+00 -4.97950345e-01 8.33990946e-02 7.20715746e-02 -5.83702683e-01 9.75522757e-01 -1.87047720e-01 -7.53017545e-01 -3.02529991e-01 -8.32531393e-01 -1.11511576e+00 -7.52108932e-01 1.32295489e-02 7.79450595e-01 -3.02598774e-01 5.97861648e-01 -7.14160055e-02 1.34453237e-01 -8.03065240e-01 8.28253776e-02 7.12444723e-01 1.23817694e+00 -4.35270429e-01 6.66508496e-01 1.19539700e-01 7.32550323e-01 -4.13661599e-01 -1.14033532e+00 3.38298291e-01 -2.95208812e-01 -4.81937230e-01 -6.51875436e-01 5.84542274e-01 -7.05674887e-01 -3.37417066e-01 1.34255636e+00 1.70550331e-01 -2.15820059e-01 1.19939828e+00 -1.44473767e+00 -7.57183194e-01 9.42719638e-01 -3.10795158e-01 -1.35555416e-01 -3.86015475e-01 -3.63457836e-02 1.22361612e+00 -6.24707103e-01 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3faf3609-7576-4b69-a7c0-a68305680a5d	a-tale-of-color-variants-representation-and	2112.0291	null	https://arxiv.org/abs/2112.02910v1	https://arxiv.org/pdf/2112.02910v1.pdf	A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce	In this paper, we address a crucial problem in fashion e-commerce (with respect to customer experience, as well as revenue): color variants identification, i.e., identifying fashion products that match exactly in their design (or style), but only to differ in their color. We propose a generic framework, that leverages deep visual Representation Learning at its heart, to address this problem for our fashion e-commerce platform. Our framework could be trained with supervisory signals in the form of triplets, that are obtained manually. However, it is infeasible to obtain manual annotations for the entire huge collection of data usually present in fashion e-commerce platforms, such as ours, while capturing all the difficult corner cases. But, to our rescue, interestingly we observed that this crucial problem in fashion e-commerce could also be solved by simple color jitter based image augmentation, that recently became widely popular in the contrastive Self-Supervised Learning (SSL) literature, that seeks to learn visual representations without using manual labels. This naturally led to a question in our mind: Could we leverage SSL in our use-case, and still obtain comparable performance to our supervised framework? The answer is, Yes! because, color variant fashion objects are nothing but manifestations of a style, in different colors, and a model trained to be invariant to the color (with, or without supervision), should be able to recognize this! This is what the paper further demonstrates, both qualitatively, and quantitatively, while evaluating a couple of state-of-the-art SSL techniques, and also proposing a novel method.	['Abhinav Ravi', 'Maulik Parmar', 'Sandeep Repakula', 'Ujjal Kr Dutta']	2021-12-06	null	null	null	null	['image-augmentation']	['computer-vision']	[ 2.81552106e-01 -7.57349581e-02 -1.13788523e-01 -2.87064046e-01 -3.34723443e-01 -1.27863157e+00 3.88257295e-01 1.12924978e-01 -4.12742943e-02 2.76435643e-01 -1.44365519e-01 -3.91705066e-01 -1.25262573e-01 -5.84694386e-01 -8.82310748e-01 -5.73658168e-01 1.18710473e-01 4.01542932e-01 -3.51961851e-01 -5.48211038e-01 1.74382806e-01 3.35751235e-01 -1.51076293e+00 2.82687068e-01 4.72019523e-01 1.32141280e+00 -2.79522777e-01 5.64929187e-01 -7.82275870e-02 7.19204903e-01 -4.66045141e-01 -9.77953494e-01 6.61033630e-01 -4.79829401e-01 -7.66228437e-01 7.52470016e-01 8.27235103e-01 -8.64594430e-02 5.45723774e-02 1.14186466e+00 -7.38004372e-02 -1.18778691e-01 5.82141280e-01 -1.40390635e+00 -1.35696435e+00 2.81770438e-01 -5.93680799e-01 -1.87853992e-01 2.91846097e-01 3.12498093e-01 1.10452628e+00 -4.77946609e-01 6.83941245e-01 9.50607598e-01 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-4.54111934e-01 -4.65501845e-01 -9.67407584e-01 1.09756327e+00 4.31764960e-01 -5.38719952e-01 -2.92733192e-01 2.33879656e-01]	[11.058286666870117, 0.1167890876531601]
4ddf2d6a-0dd4-42ec-ba6f-b2622d4b5c3c	weakly-supervised-video-summarization-using	null	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Sijia_Cai_Weakly-supervised_Video_Summarization_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Sijia_Cai_Weakly-supervised_Video_Summarization_ECCV_2018_paper.pdf	Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web Prior	Video summarization is a challenging under-constrained problem because the underlying summary of a single video strongly depends on users' subjective understandings. Data-driven approaches, such as deep neural networks, can deal with the ambiguity inherent in this task to some extent, but it is extremely expensive to acquire the temporal annotations of a large-scale video dataset. To leverage the plentiful web-crawled videos to improve the performance of video summarization, we present a generative modelling framework to learn the latent semantic video representations to bridge the benchmark data and web data. Specifically, our framework couples two important components: a variational autoencoder for learning the latent semantics from web videos, and an encoder-attention-decoder for saliency estimation of raw video and summary generation. A loss term to learn the semantic matching between the generated summaries and web videos is presented, and the overall framework is further formulated into a unified conditional variational encoder-decoder, called variational encoder-summarizer-decoder (VESD). Experiments conducted on the challenging datasets CoSum and TVSum demonstrate the superior performance of the proposed VESD to existing state-of-the-art methods. The source code of this work can be found at https://github.com/cssjcai/vesd.	['WangMeng Zuo', 'Sijia Cai', 'Lei Zhang', 'Larry S. Davis']	2018-09-01	null	null	null	eccv-2018-9	['supervised-video-summarization']	['computer-vision']	[ 2.86488622e-01 6.83792401e-03 -2.43233845e-01 -4.21864986e-01 -1.21081209e+00 -3.08284104e-01 5.15020072e-01 -1.21880680e-01 -8.73413533e-02 5.47912538e-01 7.46257305e-01 1.88056916e-01 2.93206125e-01 -3.00684154e-01 -9.94992733e-01 -6.75257862e-01 3.53094369e-01 -4.84824292e-02 3.00930351e-01 9.68787149e-02 2.32967600e-01 -4.07137036e-01 -1.59495282e+00 5.79282224e-01 1.09150219e+00 1.09204483e+00 7.71276295e-01 5.81351519e-01 2.16183718e-02 1.03312087e+00 -4.04719025e-01 -3.42496812e-01 -1.63419604e-01 -6.77385330e-01 -4.72558171e-01 4.29041505e-01 3.93839538e-01 -7.36100912e-01 -7.61624694e-01 1.15974379e+00 4.88619715e-01 2.87306309e-01 6.45976901e-01 -1.29888892e+00 -8.64943743e-01 5.67183673e-01 -5.09664178e-01 2.52696633e-01 4.10787642e-01 1.09168673e-02 1.20207906e+00 -1.12004936e+00 6.32061243e-01 1.06447256e+00 1.42040610e-01 3.75411272e-01 -8.56524706e-01 -3.81708086e-01 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e3d567d8-53d9-46ba-857e-d5310c0bec92	explaining-graph-neural-networks-via-non	2301.0278	null	https://arxiv.org/abs/2301.02780v1	https://arxiv.org/pdf/2301.02780v1.pdf	Explaining Graph Neural Networks via Non-parametric Subgraph Matching	The great success in graph neural networks (GNNs) provokes the question about explainability: Which fraction of the input graph is the most determinant of the prediction? Particularly, parametric explainers prevail in existing approaches because of their stronger capability to decipher the black-box (i.e., the target GNN). In this paper, based on the observation that graphs typically share some joint motif patterns, we propose a novel non-parametric subgraph matching framework, dubbed MatchExplainer, to explore explanatory subgraphs. It couples the target graph with other counterpart instances and identifies the most crucial joint substructure by minimizing the node corresponding-based distance. Moreover, we note that present graph sampling or node-dropping methods usually suffer from the false positive sampling problem. To ameliorate that issue, we design a new augmentation paradigm named MatchDrop. It takes advantage of MatchExplainer to fix the most informative portion of the graph and merely operates graph augmentations on the rest less informative part. We conduct extensive experiments on both synthetic and real-world datasets and show the effectiveness of our MatchExplainer by outperforming all parametric baselines with significant margins. Additional results also demonstrate that our MatchDrop is a general scheme to be equipped with GNNs for enhanced performance.	['Stan Z. Li', 'Zhangming Niu', 'Xurui Jin', 'Yinghui Jiang', 'Dragomir Radev', 'Lirong Wu', 'Siyuan Li', 'Fang Wu']	2023-01-07	null	null	null	null	['graph-sampling']	['graphs']	[ 3.36262286e-01 6.56601131e-01 -3.99591267e-01 -2.21473455e-01 -2.46012732e-01 -4.47748035e-01 4.59038109e-01 -1.31115392e-01 2.53578246e-01 8.09145033e-01 7.55523145e-02 -4.88214880e-01 -3.44645768e-01 -8.84896636e-01 -1.20365453e+00 -7.25166380e-01 -9.55558419e-02 5.73189139e-01 5.68338595e-02 -9.97258052e-02 -7.19814226e-02 2.60075331e-01 -1.07871103e+00 -3.13982628e-02 1.24244881e+00 6.23794675e-01 2.16110140e-01 4.79956865e-02 -9.61273238e-02 5.77700317e-01 -2.92718112e-01 -6.14542246e-01 3.20771962e-01 -5.12006104e-01 -5.40914893e-01 1.56868652e-01 3.89071047e-01 1.72077529e-02 -7.50954390e-01 1.11783624e+00 1.82078674e-01 1.29763000e-02 5.14355898e-01 -1.66901708e+00 -7.90139794e-01 1.21602464e+00 -7.33479619e-01 1.72207713e-01 9.93796214e-02 1.85822785e-01 1.41636300e+00 -8.29684436e-01 5.60911298e-01 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aed09d74-8783-4aef-959c-096d97dde9a1	deepsat-v2-feature-augmented-convolutional	1911.07747	null	https://arxiv.org/abs/1911.07747v1	https://arxiv.org/pdf/1911.07747v1.pdf	DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification	Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. In a preliminary version of this work, we introduced two new high resolution satellite imagery datasets (SAT-4 and SAT-6) and proposed DeepSat framework for classification based on "handcrafted" features and a deep belief network (DBN). The present paper is an extended version, we present an end-to-end framework leveraging an improved architecture that augments a convolutional neural network (CNN) with handcrafted features (instead of using DBN-based architecture) for classification. Our framework, having access to fused spatial information obtained from handcrafted features as well as CNN feature maps, have achieved accuracies of 99.90% and 99.84% respectively, on SAT-4 and SAT-6, surpassing all the other state-of-the-art results. A statistical analysis based on Distribution Separability Criterion substantiates the robustness of our approach in learning better representations for satellite imagery.	['Manohar Karki', 'Robert DiBiano', 'Supratik Mukhopadhyay', 'Qun Liu', 'Sangram Ganguly', 'Saikat Basu', 'Ramakrishna Nemani']	2019-11-15	null	null	null	null	['satellite-image-classification']	['computer-vision']	[ 1.31073162e-01 -1.74505889e-01 -8.05171859e-03 -5.49042106e-01 -7.72969902e-01 -5.44287145e-01 8.22335720e-01 4.04726267e-02 -5.34625173e-01 1.01220059e+00 -6.09050430e-02 -3.24550897e-01 -6.52792752e-01 -1.13461030e+00 -5.01844823e-01 -8.98020566e-01 -6.29168749e-01 4.39238638e-01 1.04650311e-01 -5.20108581e-01 -4.33739927e-03 8.57231557e-01 -1.78689897e+00 2.95977771e-01 7.93769419e-01 1.33669460e+00 1.23189956e-01 6.20615244e-01 1.33397937e-01 6.17950559e-01 -4.66704518e-02 2.52484530e-01 5.47408760e-01 -3.23215611e-02 -9.11716282e-01 1.44818187e-01 7.60152519e-01 -2.54345000e-01 -3.80603373e-01 9.39753711e-01 3.32129806e-01 2.40544200e-01 6.25754535e-01 -9.30215001e-01 -6.37569785e-01 3.72047752e-01 -4.21530157e-01 6.58540547e-01 -3.09041440e-01 3.67362350e-02 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fb75761b-169b-468a-9b6f-f674661de96e	subjective-quality-assessment-for-images	2206.05008	null	https://arxiv.org/abs/2206.05008v1	https://arxiv.org/pdf/2206.05008v1.pdf	Subjective Quality Assessment for Images Generated by Computer Graphics	With the development of rendering techniques, computer graphics generated images (CGIs) have been widely used in practical application scenarios such as architecture design, video games, simulators, movies, etc. Different from natural scene images (NSIs), the distortions of CGIs are usually caused by poor rending settings and limited computation resources. What's more, some CGIs may also suffer from compression distortions in transmission systems like cloud gaming and stream media. However, limited work has been put forward to tackle the problem of computer graphics generated images' quality assessment (CG-IQA). Therefore, in this paper, we establish a large-scale subjective CG-IQA database to deal with the challenge of CG-IQA tasks. We collect 25,454 in-the-wild CGIs through previous databases and personal collection. After data cleaning, we carefully select 1,200 CGIs to conduct the subjective experiment. Several popular no-reference image quality assessment (NR-IQA) methods are tested on our database. The experimental results show that the handcrafted-based methods achieve low correlation with subjective judgment and deep learning based methods obtain relatively better performance, which demonstrates that the current NR-IQA models are not suitable for CG-IQA tasks and more effective models are urgently needed.	['Guangtao Zhai', 'Wei Lu', 'Xiongkuo Min', 'Wei Sun', 'ZiCheng Zhang', 'Tao Wang']	2022-06-10	null	null	null	null	['no-reference-image-quality-assessment']	['computer-vision']	[ 0.01248805 -0.6255926 0.22868577 -0.48040935 -0.6074113 -0.03752552 0.33964354 -0.11814548 -0.46556956 0.49885872 0.15807751 -0.08786611 -0.15900053 -0.90852743 -0.37838525 -0.6490233 -0.09047694 0.04089455 0.40666977 -0.4345676 0.3477907 0.3068731 -1.5851626 0.27512947 0.9769487 1.2206628 0.3117516 0.70419323 -0.07226881 0.871982 -1.0669577 -0.7365008 0.3113603 -0.40348804 -0.38085496 -0.03661719 0.34932473 -0.7958959 -0.5047414 1.1998166 0.9154539 0.21298705 0.31973848 -1.4649475 -0.6379285 0.11817469 -0.5657923 0.5070463 0.2913654 0.34897763 0.7776631 -0.85248536 0.36966366 1.3350197 0.4814164 0.24264379 -0.57932997 -0.91732204 -0.19794512 0.69063544 -1.4278511 -0.40042114 0.5498908 -0.12624359 0.6079552 0.31578082 0.46662444 0.8143488 0.11558092 0.6835055 1.0660444 -0.1862531 0.23544468 -0.05767599 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cbf34b46-9e8c-4b8c-a4ee-9ff8837b5b13	introduction-to-protein-structure	2307.02169	null	https://arxiv.org/abs/2307.02169v2	https://arxiv.org/pdf/2307.02169v2.pdf	Introduction to Protein Structure	While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. Within the living cell, protein molecules perform specific functions, typically by interacting with other proteins, DNA, RNA or small molecules. They take on a specific three dimensional structure, encoded by its amino acid sequence, which allows them to function within the cell. Hence, the understanding of a protein's function is tightly coupled to its sequence and its three dimensional structure. Before going into protein structure analysis and prediction, and protein folding and dynamics, here, we give a short and concise introduction into the basics of protein structures.	['Sanne Abeln', 'K. Anton Feenstra', 'Laura Hoekstra', 'Jose Gavaldá-Garciá', 'Olga Ivanova', 'Bas Stringer', 'Halima Mouhib', 'Erik van Dijk', 'Annika Jacobsen']	2023-07-05	null	null	null	null	['protein-structure-prediction', 'protein-folding']	['miscellaneous', 'natural-language-processing']	[ 3.40722710e-01 -9.73070189e-02 -2.46383294e-01 -1.89455971e-01 -1.09504862e-02 -6.64771199e-01 1.92964301e-02 3.60015094e-01 -2.23519519e-01 1.23232865e+00 -1.42063290e-01 -5.79270661e-01 1.31502435e-01 -4.31641310e-01 -6.23378336e-01 -1.30053473e+00 -1.81336015e-01 5.26887596e-01 2.73586690e-01 -3.43329370e-01 1.84423923e-01 8.24603081e-01 -1.43856418e+00 1.52494898e-02 8.13808262e-01 4.09999996e-01 7.34229922e-01 8.64936948e-01 -2.20797345e-01 7.80837536e-01 -3.62908542e-01 -3.60423952e-01 -3.78504604e-01 -8.36584389e-01 -9.11849499e-01 -7.64391525e-03 -3.68825525e-01 2.24408329e-01 2.57199734e-01 4.98418689e-01 4.15828526e-01 -1.41011938e-01 7.63784885e-01 -4.65314716e-01 -6.18692815e-01 -2.11071491e-01 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af11b2fc-030b-4b04-9ee2-af9b01ab0419	a-residual-encoder-decoder-network-for	2201.05963	null	https://arxiv.org/abs/2201.05963v1	https://arxiv.org/pdf/2201.05963v1.pdf	A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening	Diabetic retinopathy refers to the pathology of the retina induced by diabetes and is one of the leading causes of preventable blindness in the world. Early detection of diabetic retinopathy is critical to avoid vision problem through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of lesion and low contrast of the images. Thus, computer-assisted diagnosis of diabetic retinopathy based on the detection of red lesions is actively being explored recently. In this paper, we present a convolutional neural network with residual skip connection for the segmentation of exudates in retinal images. To improve the performance of network architecture, a suitable image augmentation technique is used. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. Comparative performance analysis of three benchmark databases: HEI-MED, E-ophtha, and DiaretDB1 is presented. It is shown that the proposed method achieves accuracy (0.98, 0.99, 0.98) and sensitivity (0.97, 0.92, and 0.95) on E-ophtha, HEI-MED, and DiaReTDB1, respectively.	['Syed S. Naqvi', 'Muhammad Arsalan', 'Ahsan Saadat', 'Tariq M. Khan', 'Malik A. Manan']	2022-01-16	null	null	null	null	['image-augmentation']	['computer-vision']	[ 1.16404213e-01 -2.48240635e-01 1.67626649e-01 -1.84744924e-01 -7.98772499e-02 -1.62981823e-01 4.47546244e-02 -9.75891668e-03 -5.44507205e-01 6.69005692e-01 -8.01387355e-02 -5.19809663e-01 -1.20851561e-01 -6.89789653e-01 -2.49510482e-01 -7.92746186e-01 1.29608810e-01 -1.52890861e-01 3.94881487e-01 1.23689637e-01 3.36920291e-01 7.06332803e-01 -1.68039382e+00 1.86357260e-01 1.52240980e+00 1.11393428e+00 2.34516501e-01 7.49417305e-01 1.76815856e-02 7.04731941e-01 -2.96429455e-01 -2.78390288e-01 3.99168581e-01 -6.41335368e-01 -5.26758492e-01 4.59653586e-01 6.67064786e-01 -6.96416378e-01 -2.83370912e-01 1.25591636e+00 6.96435988e-01 -1.58752620e-01 5.55919945e-01 -5.09733319e-01 -5.32342017e-01 -3.18228364e-01 -8.29766572e-01 5.34862638e-01 -2.90760458e-01 2.82541245e-01 1.33175552e-01 -5.20473659e-01 2.20418781e-01 8.94064069e-01 3.30143929e-01 4.35146600e-01 -9.37988341e-01 -4.32580531e-01 -3.27400982e-01 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29db7105-d0bd-4d51-b327-e083bf0591b6	dgcnn-disordered-graph-convolutional-neural	1712.03563	null	http://arxiv.org/abs/1712.03563v1	http://arxiv.org/pdf/1712.03563v1.pdf	DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model	Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs. Graph CNNs are attracting increasing attention due to their effectiveness and efficiency. However, the existing convolution approaches focus only on regular data forms and require the transfer of the graph or key node neighborhoods of the graph into the same fixed form. During this transfer process, structural information of the graph can be lost, and some redundant information can be incorporated. To overcome this problem, we propose the disordered graph convolutional neural network (DGCNN) based on the mixed Gaussian model, which extends the CNN by adding a preprocessing layer called the disordered graph convolutional layer (DGCL). The DGCL uses a mixed Gaussian function to realize the mapping between the convolution kernel and the nodes in the neighborhood of the graph. The output of the DGCL is the input of the CNN. We further implement a backward-propagation optimization process of the convolutional layer by which we incorporate the feature-learning model of the irregular node neighborhood structure into the network. Thereafter, the optimization of the convolution kernel becomes part of the neural network learning process. The DGCNN can accept arbitrary scaled and disordered neighborhood graph structures as the receptive fields of CNNs, which reduces information loss during graph transformation. Finally, we perform experiments on multiple standard graph datasets. The results show that the proposed method outperforms the state-of-the-art methods in graph classification and retrieval.	['Yang Liu', 'Lei Huang', 'Bo Wu', 'Bo Lang']	2017-12-10	null	null	null	null	['graph-similarity']	['graphs']	[-1.10984351e-02 7.51842260e-02 4.31339592e-02 -2.51500040e-01 2.68629730e-01 -3.07377785e-01 4.27401572e-01 2.97956973e-01 -3.62647235e-01 7.36332983e-02 -1.32718161e-01 -3.41257423e-01 4.78290804e-02 -1.53347647e+00 -7.36310363e-01 -6.81834102e-01 2.83230871e-01 1.82942688e-01 4.09722567e-01 -1.01086564e-01 6.72528148e-02 7.50342786e-01 -1.14467800e+00 1.34419084e-01 9.47216451e-01 1.14571226e+00 2.09776103e-01 3.80587280e-01 -5.91887534e-01 5.43815792e-01 -4.05756801e-01 -3.49020272e-01 2.16763169e-01 -3.51131648e-01 -6.08401000e-01 -8.46309215e-02 3.06496531e-01 -1.10836253e-01 -9.66539443e-01 1.47811067e+00 5.81110716e-01 4.57441509e-01 4.00981247e-01 -1.29424047e+00 -1.18397176e+00 4.50640380e-01 -1.93395063e-01 -1.24113418e-01 1.52609035e-01 -4.40334007e-02 8.85965168e-01 -6.95985556e-01 4.32867020e-01 1.24147832e+00 7.47357130e-01 2.26790488e-01 -9.30420637e-01 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89817829-e129-488d-b203-4272fd236d20	learning-segmentation-masks-with-the	1811.04682	null	http://arxiv.org/abs/1811.04682v2	http://arxiv.org/pdf/1811.04682v2.pdf	Learning Segmentation Masks with the Independence Prior	An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances' poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.	['Xiaoqiang Li', 'Weiqin Tong', 'Pin Wu', 'Yimin Chen', 'Songmin Dai', 'Lu Wang']	2018-11-12	null	null	null	null	['foreground-segmentation']	['computer-vision']	[ 9.10300434e-01 1.02875519e+00 -1.02098778e-01 -3.90805751e-01 -9.56474364e-01 -8.11047256e-01 6.14140153e-01 -2.97250807e-01 -1.27633318e-01 6.54667377e-01 -4.01685297e-01 6.80722529e-03 3.64592820e-01 -9.76409495e-01 -1.30943251e+00 -9.05392468e-01 3.50470036e-01 8.93534243e-01 5.33905685e-01 1.40392616e-01 -7.70510808e-02 3.51110101e-01 -1.50010478e+00 5.84475935e-01 1.08685267e+00 8.73624086e-01 3.04445744e-01 5.41270614e-01 -2.93481797e-01 6.17173076e-01 -9.10443425e-01 -6.52909696e-01 8.29282880e-01 -5.88326514e-01 -8.34218085e-01 8.32253158e-01 5.49695015e-01 -1.86942443e-01 2.07687348e-01 1.20809102e+00 -1.05633371e-01 -1.50793016e-01 8.52618635e-01 -1.50578439e+00 -5.89005947e-01 8.31521630e-01 -7.58006155e-01 -5.45749843e-01 -9.52230468e-02 5.51766336e-01 7.89510787e-01 -4.79469448e-01 6.82553291e-01 1.10719013e+00 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1.67793892e-02 -8.58304679e-01 -7.58163214e-01 9.39958036e-01 1.52501777e-01 -5.76526642e-01 -7.13076353e-01 6.26247406e-01]	[10.959456443786621, -0.2004670947790146]
20ae0dc5-9d6d-449e-99d5-5eb868a7c3c5	unsupervised-image-classification-through	2009.08309	null	http://arxiv.org/abs/2009.08309v1	http://arxiv.org/pdf/2009.08309v1.pdf	Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network	We present results of a deep photonic spiking convolutional neural network, based on two-section VCSELs, targeting image classification. Training is based on unsupervised spike-timing dependent plasticity, whereas neuron time-multiplexing and ultra-fast response are exploited towards a a reduction of the physical neuron count by 90%	[]	2020-09-16	null	null	null	null	['unsupervised-image-classification']	['computer-vision']	[ 4.22659963e-01 1.55676026e-02 5.20038068e-01 1.89268161e-02 9.02883634e-02 -5.71935356e-01 1.60111517e-01 -1.19964212e-01 -1.12588549e+00 1.19498014e+00 -6.27046645e-01 -3.14464658e-01 -1.71467602e-01 -7.51691639e-01 -7.78622270e-01 -1.35382020e+00 6.09843107e-03 1.77811645e-02 7.45070159e-01 -2.58199722e-02 5.90262890e-01 5.05061984e-01 -2.06157112e+00 4.28482831e-01 2.98037797e-01 1.85434616e+00 5.40202975e-01 3.07554513e-01 -1.18488453e-01 2.47148633e-01 -6.23438239e-01 3.02992612e-02 2.13437483e-01 -2.94549316e-01 -9.68039110e-02 -7.47782826e-01 -1.06230244e-01 5.13831615e-01 -6.66164339e-01 9.83504355e-01 5.19840360e-01 -3.25212777e-01 6.92722917e-01 -8.39453638e-01 -4.70535338e-01 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06fe55d1-97b4-42e5-ae71-569671ff3393	an-adaptive-artificial-neural-network-based	2101.1241	null	https://arxiv.org/abs/2101.12410v1	https://arxiv.org/pdf/2101.12410v1.pdf	An adaptive artificial neural network-based generative design method for layout designs	Layout designs are encountered in a variety of fields. For problems with many design degrees of freedom, efficiency of design methods becomes a major concern. In recent years, machine learning methods such as artificial neural networks have been used increasingly to speed up the design process. A main issue of many such approaches is the need for a large corpus of training data that are generated using high-dimensional simulations. The high computational cost associated with training data generation largely diminishes the efficiency gained by using machine learning methods. In this work, an adaptive artificial neural network-based generative design approach is proposed and developed. This method uses a generative adversarial network to generate design candidates and thus the number of design variables is greatly reduced. To speed up the evaluation of the objective function, a convolutional neural network is constructed as the surrogate model for function evaluation. The inverse design is carried out using the genetic algorithm in conjunction with two neural networks. A novel adaptive learning and optimization strategy is proposed, which allows the design space to be effectively explored for the search for optimal solutions. As such the number of training data needed is greatly reduced. The performance of the proposed design method is demonstrated on two heat source layout design problems. In both problems, optimal designs have been obtained. Compared with several existing approaches, the proposed approach has the best performance in terms of accuracy and efficiency.	['Wenjing Ye', 'Renkai Tan', 'Chao Qian']	2021-01-29	null	null	null	null	['layout-design']	['computer-vision']	[ 2.21089348e-01 -9.73978862e-02 1.26033351e-01 5.10827219e-03 -4.00673032e-01 -2.97312498e-01 3.83224875e-01 -7.13709742e-02 -1.74326092e-01 9.77693141e-01 -2.47535452e-01 -1.84289739e-01 -3.31390172e-01 -1.06140363e+00 -3.80837917e-01 -8.19153488e-01 2.69508749e-01 2.76356816e-01 -2.24203259e-01 -1.65569872e-01 4.99663502e-01 6.87599421e-01 -1.42111206e+00 -2.44460210e-01 1.10713899e+00 1.00531638e+00 2.22743839e-01 3.64722729e-01 -9.01568979e-02 3.07824671e-01 -8.20982158e-01 -1.02790155e-01 2.60341763e-01 -6.86714709e-01 -4.10417587e-01 -1.34069249e-01 -3.00784051e-01 -1.54206650e-02 -6.93361387e-02 7.93006897e-01 9.86714482e-01 5.17607987e-01 8.40116084e-01 -9.54368114e-01 -3.93194288e-01 2.94284582e-01 -3.80618721e-01 -3.21805537e-01 2.47931536e-02 1.44915193e-01 6.20988369e-01 -9.23164606e-01 2.67758548e-01 1.05155337e+00 6.12076938e-01 3.13945472e-01 -1.44951427e+00 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9.20801312e-02 -1.58420101e-01 -1.02234018e+00 8.93385708e-03 -2.24382028e-01]	[5.92638635635376, 3.3302972316741943]
0e1c7dc7-20c6-4e4b-b75f-3fc32826eb8b	revisiting-embodiedqa-a-simple-baseline-and	1904.04166	null	https://arxiv.org/abs/1904.04166v2	https://arxiv.org/pdf/1904.04166v2.pdf	Revisiting EmbodiedQA: A Simple Baseline and Beyond	In Embodied Question Answering (EmbodiedQA), an agent interacts with an environment to gather necessary information for answering user questions. Existing works have laid a solid foundation towards solving this interesting problem. But the current performance, especially in navigation, suggests that EmbodiedQA might be too challenging for the contemporary approaches. In this paper, we empirically study this problem and introduce 1) a simple yet effective baseline that achieves promising performance; 2) an easier and practical setting for EmbodiedQA where an agent has a chance to adapt the trained model to a new environment before it actually answers users questions. In this new setting, we randomly place a few objects in new environments, and upgrade the agent policy by a distillation network to retain the generalization ability from the trained model. On the EmbodiedQA v1 benchmark, under the standard setting, our simple baseline achieves very competitive results to the-state-of-the-art; in the new setting, we found the introduced small change in settings yields a notable gain in navigation.	['Yu Wu', 'Yi Yang', 'Lu Jiang']	2019-04-08	null	null	null	null	['embodied-question-answering']	['computer-vision']	[ 3.62269282e-02 3.68966311e-01 4.05079663e-01 -4.73286659e-01 -1.06322527e+00 -8.24466825e-01 6.43044591e-01 -8.32434967e-02 -7.61578798e-01 6.37858272e-01 3.36002558e-01 -5.31661510e-01 -3.26131582e-02 -8.58242095e-01 -8.96660388e-01 -6.14187121e-01 -7.29412585e-02 7.46382475e-01 5.07219613e-01 -8.30850065e-01 1.88977972e-01 -3.00749373e-02 -1.47523427e+00 2.57031560e-01 1.09797394e+00 5.62485874e-01 6.07178569e-01 9.87071455e-01 -2.44174257e-01 1.18536294e+00 -4.94424224e-01 -5.85047066e-01 2.21863806e-01 -6.52711451e-01 -1.32822871e+00 -3.55507225e-01 5.42848051e-01 -7.34001398e-01 -4.08965617e-01 7.99970627e-01 6.50777578e-01 6.76636338e-01 3.26881021e-01 -1.11556065e+00 -9.64628756e-01 4.59691346e-01 -4.83389050e-02 2.05272809e-01 9.40305054e-01 3.89025718e-01 1.10889828e+00 -7.27752626e-01 6.74120247e-01 1.13970673e+00 5.10227740e-01 9.85272944e-01 -7.17106402e-01 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2.71122336e-01 6.82066381e-01 -5.40911913e-01 -3.92401785e-01 -3.13877344e-01]	[4.417631149291992, 0.6066211462020874]
4ad00b37-a4da-4a0f-b001-59220bc9cd98	local2global-scaling-global-representation	2107.12224	null	https://arxiv.org/abs/2107.12224v1	https://arxiv.org/pdf/2107.12224v1.pdf	Local2Global: Scaling global representation learning on graphs via local training	We propose a decentralised "local2global" approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently. In a second step, we combine the local representations into a globally consistent representation by estimating the set of rigid motions that best align the local representations using information from the patch overlaps, via group synchronization. A key distinguishing feature of local2global relative to existing work is that patches are trained independently without the need for the often costly parameter synchronisation during distributed training. This allows local2global to scale to large-scale industrial applications, where the input graph may not even fit into memory and may be stored in a distributed manner. Preliminary results on medium-scale data sets (up to $\sim$7K nodes and $\sim$200K edges) are promising, with a graph reconstruction performance for local2global that is comparable to that of globally trained embeddings. A thorough evaluation of local2global on large scale data and applications to downstream tasks, such as node classification and link prediction, constitutes ongoing work.	['Mihai Cucuringu', 'Marya Bazzi', 'Xiaowen Dong', 'Giovanni Colavizza', 'Lucas G. S. Jeub']	2021-07-26	null	null	null	null	['graph-reconstruction']	['graphs']	[-2.65230536e-01 5.80196798e-01 -5.31603634e-01 -8.50137174e-02 -5.65646589e-01 -5.51637113e-01 4.40041393e-01 6.95595503e-01 1.93538338e-01 4.20406580e-01 1.12041287e-01 -3.50199699e-01 -2.41758585e-01 -1.03046978e+00 -6.91264749e-01 -7.81363785e-01 -4.81753260e-01 6.68500841e-01 3.69477868e-01 -1.00134172e-01 4.43621837e-02 7.81201124e-01 -9.87206757e-01 -7.37083033e-02 5.21772876e-02 7.12044775e-01 2.85468847e-01 7.45660722e-01 1.52688384e-01 4.69930977e-01 -3.89263034e-01 8.12991336e-02 2.97364801e-01 -2.98837900e-01 -1.04382384e+00 1.61061272e-01 5.16994596e-01 4.40504178e-02 -4.35290754e-01 6.64826989e-01 3.64554405e-01 3.44055235e-01 4.62393939e-01 -8.42845380e-01 -4.62560207e-01 3.33721071e-01 -5.12393296e-01 2.40026966e-01 2.31532633e-01 -9.54500958e-02 1.15706170e+00 -7.18936145e-01 1.02733016e+00 1.14641607e+00 9.46974635e-01 3.74347456e-02 -1.74063110e+00 -3.22539330e-01 4.93150234e-01 -2.16662019e-01 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c2d06c66-0405-4d1c-a389-e0f43df4348e	low-frequency-image-deep-steganography	2303.13713	null	https://arxiv.org/abs/2303.13713v1	https://arxiv.org/pdf/2303.13713v1.pdf	Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness	Image deep steganography (IDS) is a technique that utilizes deep learning to embed a secret image invisibly into a cover image to generate a container image. However, the container images generated by convolutional neural networks (CNNs) are vulnerable to attacks that distort their high-frequency components. To address this problem, we propose a novel method called Low-frequency Image Deep Steganography (LIDS) that allows frequency distribution manipulation in the embedding process. LIDS extracts a feature map from the secret image and adds it to the cover image to yield the container image. The container image is not directly output by the CNNs, and thus, it does not contain high-frequency artifacts. The extracted feature map is regulated by a frequency loss to ensure that its frequency distribution mainly concentrates on the low-frequency domain. To further enhance robustness, an attack layer is inserted to damage the container image. The retrieval network then retrieves a recovered secret image from a damaged container image. Our experiments demonstrate that LIDS outperforms state-of-the-art methods in terms of robustness, while maintaining high fidelity and specificity. By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images.	['Wanlei Zhou', 'Xin Yu', 'Bo Liu', 'Yuan Zhao', 'Tianqing Zhu', 'Huajie Chen']	2023-03-23	null	null	null	null	['specificity']	['natural-language-processing']	[ 4.69181269e-01 -4.77143936e-02 1.11144491e-01 3.10504258e-01 -4.12528127e-01 -4.99874890e-01 3.25136304e-01 -4.47808951e-01 -3.61937195e-01 1.99047536e-01 7.01769604e-04 -2.98300743e-01 2.46054724e-01 -1.24246073e+00 -8.76791120e-01 -1.29692614e+00 -9.41880643e-02 -8.40059280e-01 9.98800471e-02 -4.44249719e-01 2.42795631e-01 4.24802870e-01 -1.56499851e+00 4.13370192e-01 5.50474346e-01 1.11574388e+00 1.40005440e-01 5.00430167e-01 1.16039552e-01 4.74537760e-01 -1.09419417e+00 -1.11408025e-01 4.98551011e-01 -6.91104233e-01 -2.45294183e-01 -1.87317327e-01 -1.17366239e-01 -4.97890741e-01 -8.95074487e-01 1.39104199e+00 3.97020906e-01 -3.89652699e-01 5.01619101e-01 -1.23823380e+00 -1.05412304e+00 4.13108140e-01 -4.79828715e-01 9.12495777e-02 5.42124100e-02 4.21482295e-01 3.40253413e-01 -4.86503124e-01 5.16641259e-01 9.58299696e-01 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42b9507a-746d-422d-8d4a-f83cf7c976a2	generative-zero-shot-prompt-learning-for	2307.0283	null	https://arxiv.org/abs/2307.02830v1	https://arxiv.org/pdf/2307.02830v1.pdf	Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting	Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Existing models either encode slot descriptions and examples or design handcrafted question templates using heuristic rules, suffering from poor generalization capability or robustness. In this paper, we propose a generative zero-shot prompt learning framework for cross-domain slot filling, both improving generalization and robustness than previous work. Besides, we introduce a novel inverse prompting strategy to distinguish different slot types to avoid the multiple prediction problem, and an efficient prompt-tuning strategy to boost higher performance by only training fewer prompt parameters. Experiments and analysis demonstrate the effectiveness of our proposed framework, especially huge improvements (+13.44% F1) on the unseen slots.	['Weiran Xu', 'Jiachi Liu', 'Hao Lei', 'Jinzheng Zhao', 'Keqing He', 'Guanting Dong', 'LiWen Wang', 'Xuefeng Li']	2023-07-06	null	null	null	null	['slot-filling']	['natural-language-processing']	[ 2.18586698e-01 5.15060246e-01 -6.05031312e-01 -4.93158102e-01 -1.04532504e+00 -3.98082495e-01 5.87099731e-01 -6.86792517e-03 -3.81838977e-01 9.32231545e-01 1.49373501e-03 -3.31498474e-01 -9.53712985e-02 -9.27159905e-01 -3.51174831e-01 -3.93585205e-01 4.53665435e-01 7.51556337e-01 6.23208046e-01 -3.59693825e-01 2.04805247e-02 -1.95657209e-01 -1.50488055e+00 1.27185285e-01 1.25980484e+00 1.02244616e+00 4.51485842e-01 1.64488703e-01 -7.52804577e-01 6.18815720e-01 -5.91019511e-01 -5.03696918e-01 2.26823408e-02 -4.80460048e-01 -7.39899337e-01 2.71225423e-01 -1.88765794e-01 -2.09866583e-01 -2.15457842e-01 7.30537593e-01 4.20489579e-01 4.08438027e-01 3.89909744e-01 -1.11929715e+00 -8.60490620e-01 5.43183982e-01 -3.12788099e-01 -4.83215079e-02 2.53229350e-01 -5.89445122e-02 9.40509140e-01 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17c95988-7c19-47c2-ba21-a0bb036967d9	tiny-word-embeddings-using-globally-informed	null	null	https://aclanthology.org/2020.coling-main.103	https://aclanthology.org/2020.coling-main.103.pdf	Tiny Word Embeddings Using Globally Informed Reconstruction	We reduce the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. Previous studies in this direction created a smaller word embedding model by reconstructing pre-trained word representations from those of subwords, which allows to store only a smaller number of subword embeddings in the memory. However, previous studies that train the reconstruction models using only target words cannot reduce the model size extremely while preserving its quality. Inspired by the observation of words with similar meanings having similar embeddings, our reconstruction training learns the global relationships among words, which can be employed in various models for word embedding reconstruction. Experimental results on word similarity benchmarks show that the proposed method improves the performance of the all subword-based reconstruction models.	['Yuki Arase', 'Tomoyuki Kajiwara', 'Mao Isogawa', 'Sora Ohashi']	2020-12-01	null	null	null	coling-2020-8	['word-similarity']	['natural-language-processing']	[-2.14281693e-01 -2.36850232e-02 -5.67180634e-01 -1.99561909e-01 -3.42889547e-01 -2.10049808e-01 5.97817540e-01 5.17713904e-01 -8.57250690e-01 1.68741211e-01 7.71756172e-01 -4.86435980e-01 1.71149790e-01 -1.11860812e+00 -5.45427203e-01 -5.10531366e-01 2.17865944e-01 1.83367953e-01 3.24163586e-01 -2.87840486e-01 4.60820198e-01 1.07411534e-01 -1.58290994e+00 1.96318477e-01 6.84615254e-01 5.51739633e-01 5.49034894e-01 2.71244824e-01 -8.12495589e-01 7.19578639e-02 -3.82564008e-01 -3.58664721e-01 1.64328128e-01 -2.91390836e-01 -5.31633615e-01 -1.75209820e-01 3.29014540e-01 -3.76885325e-01 -7.01518178e-01 1.05374587e+00 3.49613458e-01 4.43785369e-01 3.67096364e-01 -6.80846870e-01 -1.58716214e+00 8.54921460e-01 -2.54485518e-01 1.41411915e-01 3.09350252e-01 -1.59255534e-01 1.45148766e+00 -1.33364618e+00 3.32847089e-01 1.41820705e+00 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9498c628-8166-46ea-b113-acbfec1c6ff0	the-gh-exin-neural-network-for-hierarchical	null	null	https://www.sciencedirect.com/science/article/pii/S0893608019302060	https://www.sciencedirect.com/science/article/pii/S0893608019302060	The GH-EXIN neural network for hierarchical clustering	Hierarchical clustering is an important tool for extracting information from data in a multi-resolution way. It is more meaningful if driven by data, as in the case of divisive algorithms, which split data until no more division is allowed. However, they have the drawback of the splitting threshold setting. The neural networks can address this problem, because they basically depend on data. The growing hierarchical GH-EXIN neural network builds a hierarchical tree in an incremental (data-driven architecture) and self-organized way. It is a top-down technique which defines the horizontal growth by means of an anisotropic region of influence, based on the novel idea of neighborhood convex hull. It also reallocates data and detects outliers by using a novel approach on all the leaves, simultaneously. Its complexity is estimated and an analysis of its user-dependent parameters is given. The advantages of the proposed approach, with regard to the best existing networks, are shown and analyzed, qualitatively and quantitatively, both in benchmark synthetic problems and in a real application (image recognition from video), in order to test the performance in building hierarchical trees. Furthermore, an important and very promising application of GH-EXIN in two-way hierarchical clustering, for the analysis of gene expression data in the study of the colorectal cancer is described.	['Gabriele Ciravegna', 'Vincenzo Randazzo', 'Pietro Barbiero', 'Giansalvo Cirrincione', 'Eros Pasero']	2020-01-01	null	null	null	neural-networks-2020-1	['self-organized-clustering']	['miscellaneous']	[ 1.46738410e-01 8.65511671e-02 5.61399423e-02 -3.90548825e-01 -1.75586089e-01 -1.55524388e-01 2.76953518e-01 6.48622096e-01 -8.01279485e-01 4.57989424e-01 1.00231305e-01 5.80661483e-02 -6.93284750e-01 -1.04669917e+00 -3.82442832e-01 -1.30147636e+00 -3.05283368e-01 8.84387553e-01 3.41604918e-01 -1.36644721e-01 2.96070606e-01 8.50856125e-01 -1.99640405e+00 2.73067534e-01 8.40374529e-01 5.54952264e-01 2.84170490e-02 3.67578417e-01 -2.40029067e-01 4.89358008e-01 -4.31576312e-01 3.28328758e-01 1.72004208e-01 -3.24364364e-01 -6.22786820e-01 4.30601895e-01 -1.24789722e-01 3.02943230e-01 5.18534482e-01 9.92439866e-01 3.50994885e-01 1.46120802e-01 1.01629126e+00 -9.08295453e-01 -1.18396608e-02 6.74554586e-01 -8.05877447e-01 5.27124330e-02 -9.92292389e-02 -3.71419489e-01 5.77784240e-01 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ac0936c4-adc0-4380-8686-bcae1527029c	unsupervised-domain-adaptation-for-semantic-5	2305.05789	null	https://arxiv.org/abs/2305.05789v2	https://arxiv.org/pdf/2305.05789v2.pdf	Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching	Semantic segmentation is a critical step in automated image interpretation and analysis where pixels are classified into one or more predefined semantically meaningful classes. Deep learning approaches for semantic segmentation rely on harnessing the power of annotated images to learn features indicative of these semantic classes. Nonetheless, they often fail to generalize when there is a significant domain (i.e., distributional) shift between the training (i.e., source) data and the dataset(s) encountered when deployed (i.e., target), necessitating manual annotations for the target data to achieve acceptable performance. This is especially important in medical imaging because different image modalities have significant intra- and inter-site variations due to protocol and vendor variability. Current techniques are sensitive to hyperparameter tuning and target dataset size. This paper presents an unsupervised domain adaptation approach for semantic segmentation that alleviates the need for annotating target data. Using kernel density estimation, we match the target data distribution to the source in the feature space, particularly when the number of target samples is limited (3% of the target dataset size). We demonstrate the efficacy of our proposed approach on 2 datasets, multisite prostate MRI and histopathology images.	['Shireen Elhabian', 'Beatrice Knudsen', 'Tushar Kataria']	2023-05-09	null	null	null	null	['unsupervised-domain-adaptation']	['methodology']	[ 7.13950813e-01 3.70648466e-02 -3.34323794e-01 -8.39939594e-01 -1.08247459e+00 -8.33265424e-01 2.76772708e-01 6.08868539e-01 -6.66157424e-01 6.04364336e-01 -7.96908811e-02 2.22621430e-02 -2.49264121e-01 -5.86859882e-01 -6.08535588e-01 -9.09496486e-01 1.32730260e-01 7.69852102e-01 2.38171741e-01 3.88212055e-01 3.22253406e-01 3.28900546e-01 -1.13235497e+00 2.17391938e-01 1.03268647e+00 9.95091140e-01 5.63750386e-01 5.68397760e-01 -3.59893590e-01 1.86821163e-01 -8.60519648e-01 5.43653360e-03 2.46135071e-01 -4.22677726e-01 -8.73264074e-01 3.20451200e-01 2.41795808e-01 1.26586661e-01 2.18364879e-01 1.49807453e+00 3.33286494e-01 3.47401132e-03 9.41200733e-01 -1.27213073e+00 -4.25225139e-01 3.89991105e-01 -6.04745686e-01 3.34374130e-01 -3.24644774e-01 8.46185982e-02 5.54861903e-01 -4.19977427e-01 6.48437321e-01 6.90937996e-01 6.60554409e-01 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4ab363d2-9b31-443a-9b7a-31f994261616	knowledge-restore-and-transfer-for-multi	2302.13334	null	https://arxiv.org/abs/2302.13334v2	https://arxiv.org/pdf/2302.13334v2.pdf	Knowledge Restore and Transfer for Multi-label Class-Incremental Learning	Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forgetting methods to solve the problem of catastrophic forgetting in class-incremental learning, these methods have difficulty in solving the MLCIL problem due to label absence and information dilution. In this paper, we propose a knowledge restore and transfer (KRT) framework for MLCIL, which includes a dynamic pseudo-label (DPL) module to restore the old class knowledge and an incremental cross-attention(ICA) module to save session-specific knowledge and transfer old class knowledge to the new model sufficiently. Besides, we propose a token loss to jointly optimize the incremental cross-attention module. Experimental results on MS-COCO and PASCAL VOC datasets demonstrate the effectiveness of our method for improving recognition performance and mitigating forgetting on multi-label class-incremental learning tasks.	['Yihong Gong', 'Xing Wei', 'Yuhang He', 'Haoyu Luo', 'Songlin Dong']	2023-02-26	null	null	null	null	['class-incremental-learning']	['computer-vision']	[ 6.64414883e-01 -2.75629293e-02 -3.78618926e-01 -5.60137391e-01 -6.97011173e-01 -2.48087451e-01 2.73679018e-01 2.14047253e-01 -4.86338705e-01 9.41483200e-01 -1.48701683e-01 -2.08516255e-01 -3.82305384e-02 -2.48886198e-01 -7.00595200e-01 -7.53553569e-01 5.21789908e-01 3.57639432e-01 2.86203712e-01 3.31241310e-01 1.18518900e-02 2.47619063e-01 -1.63491833e+00 5.70228219e-01 1.08438182e+00 7.97457814e-01 3.12544554e-01 5.03767908e-01 -3.01348865e-01 1.22570002e+00 -5.94118118e-01 -4.83056188e-01 -8.95364657e-02 -4.64447528e-01 -9.75817800e-01 7.79093057e-02 8.18638444e-01 -2.11925104e-01 -2.35112324e-01 1.00240648e+00 7.46650636e-01 1.98525175e-01 4.94496822e-01 -1.38701761e+00 -8.99508059e-01 6.08259201e-01 -7.49544680e-01 7.28028715e-02 -1.03351898e-01 -4.31949040e-03 4.93106753e-01 -1.08391142e+00 4.89460647e-01 1.30770278e+00 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336eac41-33fd-4926-8e94-844fef7d4f3d	unsupervised-writer-retrieval-using-netrvlad	2305.05358	null	https://arxiv.org/abs/2305.05358v2	https://arxiv.org/pdf/2305.05358v2.pdf	Towards Writer Retrieval for Historical Datasets	This paper presents an unsupervised approach for writer retrieval based on clustering SIFT descriptors detected at keypoint locations resulting in pseudo-cluster labels. With those cluster labels, a residual network followed by our proposed NetRVLAD, an encoding layer with reduced complexity compared to NetVLAD, is trained on 32x32 patches at keypoint locations. Additionally, we suggest a graph-based reranking algorithm called SGR to exploit similarities of the page embeddings to boost the retrieval performance. Our approach is evaluated on two historical datasets (Historical-WI and HisIR19). We include an evaluation of different backbones and NetRVLAD. It competes with related work on historical datasets without using explicit encodings. We set a new State-of-the-art on both datasets by applying our reranking scheme and show that our approach achieves comparable performance on a modern dataset as well.	['Robert Sablatnig', 'Florian Kleber', 'Marco Peer']	2023-05-09	null	null	null	null	['graph-similarity']	['graphs']	[-1.95256725e-01 -4.61192966e-01 -5.26486039e-01 -1.37588054e-01 -9.98226762e-01 -6.72202110e-01 9.16876554e-01 7.47346342e-01 -5.23587465e-01 1.61863714e-01 4.52725559e-01 -1.43002167e-01 -7.38964081e-01 -6.97560310e-01 -5.90881109e-01 -2.95713216e-01 -7.44154632e-01 6.07955635e-01 5.49610436e-01 -2.25643754e-01 7.13724971e-01 8.55079293e-01 -1.57872570e+00 4.18602318e-01 2.10312828e-01 1.07147968e+00 -2.02077106e-02 8.11531425e-01 -7.74909854e-02 7.85537302e-01 -4.73496377e-01 -2.68571395e-02 7.40161479e-01 3.43616366e-01 -8.89682829e-01 -2.74604976e-01 1.24212015e+00 -4.99525040e-01 -1.00645435e+00 6.43170774e-01 6.54992819e-01 2.95440942e-01 7.31801867e-01 -1.11058319e+00 -8.29402685e-01 5.84407866e-01 -7.74408042e-01 2.79514641e-01 5.16161144e-01 -4.79694694e-01 1.73054338e+00 -1.03809845e+00 1.08914506e+00 1.07876122e+00 6.94638848e-01 -2.23772265e-02 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4.34045970e-01 2.65734643e-01 -3.91815603e-01 -3.00822139e-01 -3.15535575e-01]	[10.7545804977417, 0.5406612753868103]
36a93646-1698-431c-90f1-eedf663e7890	motif-difference-field-a-simple-and-effective	2001.07582	null	https://arxiv.org/abs/2001.07582v1	https://arxiv.org/pdf/2001.07582v1.pdf	Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification	Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed. Compared to other image representations of time series, MDF is simple and easy to construct. With the Fully Convolution Network (FCN) as the classifier, MDF demonstrates the superior performance on the UCR time series dataset in benchmark with other time series classification methods. It is interesting to find that the triadic time series motifs give the best result in the test. Due to the motif clustering reflected in MDF, the significant motifs are detected with the help of the Gradient-weighted Class Activation Mapping (Grad-CAM). The areas in MDF with high weight in Grad-CAM have a high contribution from the significant motifs with the desired ordinal patterns associated with the signature patterns in time series. However, the signature patterns cannot be identified with the neural network classifiers directly based on the time series.	['Xin Chen', 'Yadong Zhang']	2020-01-21	null	null	null	null	['time-series-clustering']	['time-series']	[ 6.19270317e-02 -7.26100326e-01 1.68141574e-01 -2.34489694e-01 1.47977278e-01 -4.02755380e-01 5.24315298e-01 2.26410806e-01 -2.97311872e-01 3.30659717e-01 9.55514312e-02 -2.31307775e-01 -6.88497245e-01 -8.39205205e-01 -6.10412896e-01 -7.60379195e-01 -1.06321430e+00 -1.68850869e-01 1.40899308e-02 -2.98672587e-01 5.76263130e-01 6.32051170e-01 -2.08907390e+00 8.04644227e-01 6.49762690e-01 1.36963975e+00 1.69763759e-01 2.43528172e-01 -5.48009038e-01 4.28564399e-01 -6.03080153e-01 6.53170407e-01 1.90922141e-01 -4.29340780e-01 -3.60738099e-01 -2.17696980e-01 1.06770145e-02 1.07154019e-01 -3.53497624e-01 9.31914628e-01 6.69821277e-02 4.33061987e-01 5.77480614e-01 -1.40256095e+00 -3.70062232e-01 4.50840235e-01 -4.76688415e-01 7.17842460e-01 2.61391938e-01 -3.15746456e-01 9.11898017e-01 -8.65417123e-01 5.99129438e-01 9.22858894e-01 6.39616191e-01 -4.53098863e-02 -9.45859075e-01 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6.74614966e-01 1.93190247e-01 -5.27498484e-01 -3.45848650e-01 -1.57073066e-01]	[7.164590358734131, 3.011826276779175]
7d7bff70-15c2-4ddb-9fbe-6ae1fab182be	concept-representation-learning-with	2112.05677	null	https://arxiv.org/abs/2112.05677v2	https://arxiv.org/pdf/2112.05677v2.pdf	Concept Representation Learning with Contrastive Self-Supervised Learning	Concept-oriented deep learning (CODL) is a general approach to meet the future challenges for deep learning: (1) learning with little or no external supervision, (2) coping with test examples that come from a different distribution than the training examples, and (3) integrating deep learning with symbolic AI. In CODL, as in human learning, concept representations are learned based on concept exemplars. Contrastive self-supervised learning (CSSL) provides a promising approach to do so, since it: (1) uses data-driven associations, to get away from semantic labels, (2) supports incremental and continual learning, to get away from (large) fixed datasets, and (3) accommodates emergent objectives, to get away from fixed objectives (tasks). We discuss major aspects of concept representation learning using CSSL. These include dual-level concept representations, CSSL for feature representations, exemplar similarity measures and self-supervised relational reasoning, incremental and continual CSSL, and contrastive self-supervised concept (class) incremental learning. The discussion leverages recent findings from cognitive neural science and CSSL.	['Daniel T. Chang']	2021-12-10	null	null	null	null	['relational-reasoning']	['natural-language-processing']	[ 4.40384716e-01 4.27177221e-01 -2.57349819e-01 -5.38607478e-01 -1.97179317e-01 -6.79547429e-01 9.35596228e-01 7.09869385e-01 -3.31760764e-01 7.90174484e-01 1.90912351e-01 -2.15195403e-01 -8.25380862e-01 -9.43986714e-01 -5.97207665e-01 -4.99008119e-01 -5.32144368e-01 9.42112386e-01 1.14583507e-01 -4.89204913e-01 5.29240131e-01 5.26749432e-01 -2.03694034e+00 4.33470726e-01 9.66527700e-01 1.11808038e+00 2.77442597e-02 3.44981194e-01 -4.92876798e-01 1.36517334e+00 -5.45124233e-01 5.00374846e-02 -7.58057535e-02 -6.00791454e-01 -1.09360635e+00 2.67694760e-02 3.73540342e-01 1.91841394e-01 3.25839758e-01 8.22004557e-01 7.37482160e-02 5.28203547e-01 9.46645975e-01 -1.55526257e+00 -1.18811810e+00 8.18098009e-01 -4.21678163e-02 1.64751947e-01 3.02566856e-01 3.79875414e-02 9.41882432e-01 -1.01414979e+00 6.32286131e-01 1.50838053e+00 7.36967444e-01 7.69473135e-01 -1.48386610e+00 -5.23492396e-01 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3e0b9c75-5a6b-49a8-b0d9-d6b2ee8b8594	when-did-it-happen-duration-informed-temporal	2202.08138	null	https://arxiv.org/abs/2202.08138v2	https://arxiv.org/pdf/2202.08138v2.pdf	When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs	We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods, and leads to improvements over previous work for the task of temporal action localization.	['Rada Mihalcea', 'Dandan Shan', 'Jiajun Bao', 'YuHang Zhou', 'Santiago Castro', 'Oana Ignat']	2022-02-16	null	null	null	null	['action-localization']	['computer-vision']	[ 2.91390717e-01 -6.72567338e-02 -4.80557859e-01 -2.28730038e-01 -5.19095600e-01 -7.72865951e-01 8.46793473e-01 9.94543508e-02 -4.91271406e-01 6.14561081e-01 8.97400916e-01 2.35501006e-01 1.33332804e-01 -2.41295010e-01 -5.12146056e-01 -5.53941607e-01 -7.50480831e-01 -1.34148285e-01 6.10394657e-01 4.25149649e-02 2.76147830e-03 3.90271455e-01 -1.55796409e+00 8.17815125e-01 5.19181155e-02 9.18643653e-01 2.03811526e-02 7.74535835e-01 3.92391682e-01 1.93296528e+00 -5.39539874e-01 -2.64866918e-01 -1.09401040e-01 -8.39860916e-01 -1.28504288e+00 4.17689502e-01 3.85523856e-01 -4.22859818e-01 -8.61584306e-01 6.84401631e-01 2.12902188e-01 6.18130445e-01 4.06055123e-01 -1.38591075e+00 -3.96350831e-01 6.33919716e-01 -2.92600125e-01 7.02342868e-01 1.09634602e+00 7.92523921e-02 8.97109866e-01 -4.91145253e-01 1.27323079e+00 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b435fba1-7b54-4f4d-b282-d1647c158fba	data-augmented-3d-semantic-scene-completion	2111.13309	null	https://arxiv.org/abs/2111.13309v1	https://arxiv.org/pdf/2111.13309v1.pdf	Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors	Semantic scene completion (SSC) is a challenging Computer Vision task with many practical applications, from robotics to assistive computing. Its goal is to infer the 3D geometry in a field of view of a scene and the semantic labels of voxels, including occluded regions. In this work, we present SPAwN, a novel lightweight multimodal 3D deep CNN that seamlessly fuses structural data from the depth component of RGB-D images with semantic priors from a bimodal 2D segmentation network. A crucial difficulty in this field is the lack of fully labeled real-world 3D datasets which are large enough to train the current data-hungry deep 3D CNNs. In 2D computer vision tasks, many data augmentation strategies have been proposed to improve the generalization ability of CNNs. However those approaches cannot be directly applied to the RGB-D input and output volume of SSC solutions. In this paper, we introduce the use of a 3D data augmentation strategy that can be applied to multimodal SSC networks. We validate our contributions with a comprehensive and reproducible ablation study. Our solution consistently surpasses previous works with a similar level of complexity.	['Teofilo de Campos', 'Frederico Guth', 'Aloisio Dourado']	2021-11-26	null	null	null	null	['3d-semantic-scene-completion']	['computer-vision']	[ 4.84772503e-01 3.94365519e-01 2.39328116e-01 -4.83281910e-01 -5.35930395e-01 -5.78834116e-01 3.28801274e-01 5.27433194e-02 -5.31287372e-01 3.40741068e-01 -7.77418762e-02 -2.94796109e-01 8.28787684e-02 -4.39153671e-01 -7.06961811e-01 -4.24967140e-01 2.49842331e-01 6.22919917e-01 5.60144305e-01 -2.60537326e-01 1.15926474e-01 9.48727548e-01 -1.59877276e+00 2.00849697e-01 6.32064283e-01 1.22876823e+00 5.38684785e-01 4.10661131e-01 -3.84569496e-01 2.65473098e-01 -2.01718688e-01 1.32548258e-01 4.88005400e-01 -8.83754417e-02 -8.33407640e-01 2.85203487e-01 5.66180825e-01 -4.23213661e-01 -3.48360628e-01 8.31576705e-01 5.21956980e-01 1.01960078e-02 6.11340463e-01 -1.34197259e+00 -1.32344633e-01 -1.13345549e-01 -6.21478975e-01 -4.59069721e-02 2.57931679e-01 -4.20998782e-02 4.52829659e-01 -8.50147724e-01 6.35863304e-01 1.22382128e+00 6.65252030e-01 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8893bb71-819b-4e5f-8921-614ddf8263b4	mattica-smm4h22-leveraging-sentiment-for	null	null	https://aclanthology.org/2022.smm4h-1.22	https://aclanthology.org/2022.smm4h-1.22.pdf	mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning	This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022. Our team (mattica) participated in detecting stances and premises in tweets about health mandates related to COVID-19 (Task 2). Our approach was based on using an in-domain Pretrained Language Model, which we fine-tuned by combining different strategies such as leveraging an additional stance detection dataset through two-stage fine-tuning, joint-learning Stance and Premise detection objectives; and ensembling the sentiment-polarity given by an off-the-shelf fine-tuned model.	['Ljiljana Dolamic', 'Fabio Rinaldi', 'Joseph Cornelius', 'Oscar Lithgow-Serrano']	null	null	null	null	smm4h-coling-2022-10	['stance-detection']	['natural-language-processing']	[ 3.78714859e-01 8.10441077e-01 -4.41473335e-01 -6.11304879e-01 -1.06921244e+00 -5.15899658e-01 7.54949033e-01 8.66627634e-01 -6.82850778e-01 7.64359474e-01 6.78784251e-01 -4.99690026e-01 1.59787670e-01 -6.44275129e-01 -6.40226483e-01 -1.71172470e-01 -1.50033340e-01 7.12562442e-01 2.56130010e-01 -5.04383862e-01 2.17715368e-01 -2.54327804e-01 -9.16449130e-01 1.24084282e+00 5.99461854e-01 6.60483539e-01 -3.68136823e-01 7.17406750e-01 3.22836377e-02 1.10192871e+00 -5.19751728e-01 -4.97452587e-01 -1.62100017e-01 -1.87564373e-01 -1.08250701e+00 -3.49455386e-01 -1.36621967e-01 2.54918307e-01 5.73340654e-01 6.92588985e-01 4.91346776e-01 -4.93316323e-01 6.22514069e-01 -7.15128124e-01 -2.33333528e-01 1.22666991e+00 -5.92163801e-01 4.87256736e-01 6.18003190e-01 1.39869109e-01 1.25643098e+00 -6.30918980e-01 1.07546008e+00 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f91d6fc0-d4fd-4259-81e6-9c8b7d8521ea	deception-detection-in-text-and-its-relation	2105.1253	null	https://arxiv.org/abs/2105.12530v1	https://arxiv.org/pdf/2105.12530v1.pdf	Deception detection in text and its relation to the cultural dimension of individualism/collectivism	Deception detection is a task with many applications both in direct physical and in computer-mediated communication. Our focus is on automatic deception detection in text across cultures. We view culture through the prism of the individualism/collectivism dimension and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to norms in respect to the individualism/collectivism divide. We also investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features based on phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over 11 datasets from 5 languages i.e., English, Dutch, Russian, Spanish and Romanian, from six countries (US, Belgium, India, Russia, Mexico and Romania), and we applied two classification methods i.e, logistic regression and fine-tuned BERT models. The results showed that our task is fairly complex and demanding. There are indications that some linguistic cues of deception have cultural origins, and are consistent in the context of diverse domains and dataset settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in a unified manner, and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.	['Dimitris Plexousakis', 'Ion Androutsopoulos', 'Giorgos Flouris', 'Theodore Patkos', 'Panagiotis Papadakos', 'Katerina Papantoniou']	2021-05-26	null	null	null	null	['deception-detection']	['miscellaneous']	[-2.41812661e-01 -6.08503759e-01 -1.15450449e-01 -3.96163911e-01 -2.15701416e-01 -8.19360077e-01 1.20811582e+00 2.49351338e-01 -7.90052772e-01 7.74460077e-01 6.67475760e-01 -2.35980824e-01 -9.59445164e-02 -3.74409378e-01 -1.80279747e-01 -5.76114595e-01 2.91401237e-01 1.57362342e-01 -4.29432988e-01 -3.91531229e-01 9.19282973e-01 5.97176731e-01 -1.29827273e+00 1.18257403e-01 1.19230020e+00 5.83100498e-01 -4.19103086e-01 4.94289041e-01 -8.66424888e-02 4.93900985e-01 -9.95032847e-01 -8.12863767e-01 -2.32190534e-01 -5.36154687e-01 -6.02409184e-01 1.77696511e-01 4.30843860e-01 -6.06872588e-02 -2.01973021e-01 1.13267517e+00 3.32253963e-01 -1.62709355e-01 9.61105108e-01 -9.88566935e-01 -1.20866537e+00 3.17968160e-01 -3.42455387e-01 2.50368923e-01 7.34077811e-01 4.77554724e-02 5.67579329e-01 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71c4be01-5140-41dc-a35a-bd9a85fe5a9c	generalized-earley-parser-bridging-symbolic	1806.03497	null	http://arxiv.org/abs/1806.03497v1	http://arxiv.org/pdf/1806.03497v1.pdf	Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction	Future predictions on sequence data (e.g., videos or audios) require the algorithms to capture non-Markovian and compositional properties of high-level semantics. Context-free grammars are natural choices to capture such properties, but traditional grammar parsers (e.g., Earley parser) only take symbolic sentences as inputs. In this paper, we generalize the Earley parser to parse sequence data which is neither segmented nor labeled. This generalized Earley parser integrates a grammar parser with a classifier to find the optimal segmentation and labels, and makes top-down future predictions. Experiments show that our method significantly outperforms other approaches for future human activity prediction.	['Song-Chun Zhu', 'Siyuan Qi', 'Baoxiong Jia']	2018-06-09	generalized-earley-parser-bridging-symbolic-1	https://icml.cc/Conferences/2018/Schedule?showEvent=1920	http://proceedings.mlr.press/v80/qi18a/qi18a.pdf	icml-2018-7	['activity-prediction', 'activity-prediction']	['computer-vision', 'time-series']	[ 5.46561539e-01 4.78294045e-01 -5.18316448e-01 -6.72601342e-01 -4.30756629e-01 -6.55567706e-01 4.93914604e-01 1.69005960e-01 -1.11418463e-01 7.81716883e-01 2.73124397e-01 -4.43904132e-01 3.58062297e-01 -9.73638296e-01 -5.96744180e-01 -7.80209303e-02 -2.21324444e-01 2.47387215e-01 7.38348007e-01 -4.52002995e-02 2.35745072e-01 2.16771170e-01 -1.67581928e+00 5.14841020e-01 5.89942157e-01 6.88120484e-01 5.01555502e-01 1.11304867e+00 -4.89246547e-01 1.14377141e+00 -1.56613678e-01 -3.38342249e-01 -2.22310573e-01 -7.04732955e-01 -1.13846326e+00 1.23709366e-01 -3.49082910e-02 -2.11030453e-01 -9.90670770e-02 9.15616512e-01 -3.11572224e-01 2.34008893e-01 3.44614625e-01 -1.34403396e+00 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92400ad5-69c5-4d02-9236-f815c8b87df1	imaginenet-target-speaker-extraction-with	2211.00109	null	https://arxiv.org/abs/2211.00109v2	https://arxiv.org/pdf/2211.00109v2.pdf	ImagineNET: Target Speaker Extraction with Intermittent Visual Cue through Embedding Inpainting	The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual cue is not only feasible, but also effective due to its noise robustness, and becoming popular. However, it is difficult to guarantee that such parallel visual cue is always available in real-world applications where visual occlusion or intermittent communication can occur. In this paper, we study the audio-visual speaker extraction algorithms with intermittent visual cue. We propose a joint speaker extraction and visual embedding inpainting framework to explore the mutual benefits. To encourage the interaction between the two tasks, they are performed alternately with an interlacing structure and optimized jointly. We also propose two types of visual inpainting losses and study our proposed method with two types of popularly used visual embeddings. The experimental results show that we outperform the baseline in terms of signal quality, perceptual quality, and intelligibility.	['Haizhou Li', 'Marvin Borsdorf', 'Wupeng Wang', 'Zexu Pan']	2022-10-31	null	null	null	null	['target-speaker-extraction']	['audio']	[ 1.39219016e-01 -1.29574180e-01 -1.63032725e-01 1.38142258e-02 -8.81460369e-01 -3.70163918e-01 4.56610441e-01 -1.20967574e-01 -2.26474196e-01 5.03927112e-01 4.34273064e-01 7.93436356e-03 8.98889601e-02 -9.57310200e-02 -5.43381274e-01 -9.17590618e-01 2.36343384e-01 -3.07357848e-01 1.15477696e-01 2.02929646e-01 1.65994227e-01 3.92137319e-01 -1.60246074e+00 2.61796918e-02 7.87127912e-01 9.73449826e-01 7.11848438e-01 6.02870941e-01 -2.78181225e-01 6.28519595e-01 -6.68994844e-01 -9.28565562e-02 3.18730772e-01 -5.10954440e-01 -1.82595804e-01 5.42874455e-01 1.73015043e-01 -2.97036976e-01 -3.92770112e-01 1.09265482e+00 8.76996398e-01 1.52143627e-01 3.52793604e-01 -1.44146931e+00 -5.40091693e-01 4.58177328e-01 -1.14027476e+00 1.74859732e-01 5.11815071e-01 2.98427939e-01 9.05903280e-01 -9.66848254e-01 3.53751421e-01 1.12953258e+00 2.44641230e-01 5.00087202e-01 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1de20229-e86a-4bf2-866e-da4df136bd95	computation-with-sequences-in-the-brain	2306.03812	null	https://arxiv.org/abs/2306.03812v1	https://arxiv.org/pdf/2306.03812v1.pdf	Computation with Sequences in the Brain	Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain's learning capabilities remain unmatched. How cognition arises from neural activity is a central open question in neuroscience, inextricable from the study of intelligence itself. A simple formal model of neural activity was proposed in Papadimitriou [2020] and has been subsequently shown, through both mathematical proofs and simulations, to be capable of implementing certain simple cognitive operations via the creation and manipulation of assemblies of neurons. However, many intelligent behaviors rely on the ability to recognize, store, and manipulate temporal sequences of stimuli (planning, language, navigation, to list a few). Here we show that, in the same model, time can be captured naturally as precedence through synaptic weights and plasticity, and, as a result, a range of computations on sequences of assemblies can be carried out. In particular, repeated presentation of a sequence of stimuli leads to the memorization of the sequence through corresponding neural assemblies: upon future presentation of any stimulus in the sequence, the corresponding assembly and its subsequent ones will be activated, one after the other, until the end of the sequence. Finally, we show that any finite state machine can be learned in a similar way, through the presentation of appropriate patterns of sequences. Through an extension of this mechanism, the model can be shown to be capable of universal computation. We support our analysis with a number of experiments to probe the limits of learning in this model in key ways. Taken together, these results provide a concrete hypothesis for the basis of the brain's remarkable abilities to compute and learn, with sequences playing a vital role.	['Santosh S. Vempala', 'Christos H. Papadimitriou', 'Max Dabagia']	2023-06-06	null	null	null	null	['mathematical-proofs', 'memorization', 'open-question', 'temporal-sequences']	['miscellaneous', 'natural-language-processing', 'natural-language-processing', 'reasoning']	[ 5.79262793e-01 -5.42303035e-03 1.80695757e-01 4.30493616e-02 2.78620750e-01 -9.19864476e-01 1.09941018e+00 3.42094570e-01 -5.40191293e-01 8.27582002e-01 -9.68314186e-02 -3.75986934e-01 -3.49315524e-01 -8.81309748e-01 -7.31576502e-01 -9.89131749e-01 -3.29602629e-01 2.05165818e-01 4.31850195e-01 -3.74594420e-01 5.95433056e-01 6.83240116e-01 -1.74434257e+00 1.55568361e-01 3.46231997e-01 8.40351701e-01 5.95979929e-01 7.54962623e-01 -1.82017814e-02 7.24847913e-01 -3.44026774e-01 -2.51572505e-02 1.68146983e-01 -6.34444058e-01 -8.11602712e-01 3.28837074e-02 -1.36699632e-01 1.71727315e-01 -1.99810773e-01 7.39062548e-01 1.05619170e-01 2.70739317e-01 5.56998610e-01 -8.00383627e-01 -5.19382834e-01 3.63192171e-01 2.07822070e-01 4.03721333e-01 3.81809115e-01 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f07308f2-76d1-44e2-bafb-0e88302c1cd4	aigciqa2023-a-large-scale-image-quality	2307.00211	null	https://arxiv.org/abs/2307.00211v1	https://arxiv.org/pdf/2307.00211v1.pdf	AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence	In this paper, in order to get a better understanding of the human visual preferences for AIGIs, a large-scale IQA database for AIGC is established, which is named as AIGCIQA2023. We first generate over 2000 images based on 6 state-of-the-art text-to-image generation models using 100 prompts. Based on these images, a well-organized subjective experiment is conducted to assess the human visual preferences for each image from three perspectives including quality, authenticity and correspondence. Finally, based on this large-scale database, we conduct a benchmark experiment to evaluate the performance of several state-of-the-art IQA metrics on our constructed database.	['Guangtao Zhai', 'Xiongkuo Min', 'Shi Chen', 'Jing Liu', 'Huiyu Duan', 'Jiarui Wang']	2023-07-01	null	null	null	null	['image-quality-assessment', 'image-generation']	['computer-vision', 'computer-vision']	[-1.72180280e-01 -2.29719311e-01 1.58428669e-01 -3.43051523e-01 -8.65685821e-01 -5.93840301e-01 6.36135399e-01 -1.08861923e-01 -2.68688321e-01 4.59948987e-01 2.02960506e-01 -1.51372716e-01 1.59339681e-01 -8.92879486e-01 -4.88441527e-01 -3.15046400e-01 1.08316623e-01 3.53154123e-01 1.77212432e-01 -1.05153598e-01 4.22286540e-01 3.36576730e-01 -1.65217984e+00 5.07722974e-01 1.38433945e+00 1.28329408e+00 1.03130840e-01 3.63301575e-01 6.70342594e-02 5.32712936e-01 -9.10595000e-01 -6.62163556e-01 2.44793788e-01 -3.15597624e-01 -8.39022756e-01 4.33256835e-01 5.62197030e-01 -5.39440453e-01 -1.43216789e-01 1.10964692e+00 6.41709805e-01 -3.71212699e-02 7.88968265e-01 -1.38234377e+00 -1.16023970e+00 2.59412974e-01 -5.85158050e-01 -2.68828850e-02 6.82737589e-01 7.35652506e-01 1.16456759e+00 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83069fc0-1430-4a6d-9a14-570ad4702720	quiko-a-quantum-beat-generation-application	2204.0437	null	https://arxiv.org/abs/2204.04370v2	https://arxiv.org/pdf/2204.04370v2.pdf	QuiKo: A Quantum Beat Generation Application	In this chapter a quantum music generation application called QuiKo will be discussed. It combines existing quantum algorithms with data encoding methods from quantum machine learning to build drum and audio sample patterns from a database of audio tracks. QuiKo leverages the physical properties and characteristics of quantum computers to generate what can be referred to as Soft Rules proposed by Alexis Kirke. These rules take advantage of the noise produced by quantum devices to develop flexible rules and grammars for quantum music generation. These properties include qubit decoherence and phase kickback due controlled quantum gates within the quantum circuit. QuiKo builds upon the concept of soft rules in quantum music generation and takes it a step further. It attempts to mimic and react to an external musical inputs, similar to the way that human musicians play and compose with one another. Audio signals are used as inputs into the system. Feature extraction is then performed on the signal to identify the harmonic and percussive elements. This information is then encoded onto the quantum circuit. Measurements of the quantum circuit are then taken providing results in the form of probability distributions for external music applications to use to build the new drum patterns.	['Scott Oshiro']	2022-04-09	null	null	null	null	['music-generation', 'music-generation']	['audio', 'music']	[ 3.79777521e-01 -1.25040904e-01 2.10188270e-01 1.56633973e-01 -8.57094109e-01 -1.03952670e+00 4.64763701e-01 -1.30395532e-01 8.11762959e-02 6.08951688e-01 1.43850118e-01 1.96636900e-01 -1.59190208e-01 -1.24936295e+00 -5.44905186e-01 -9.53786075e-01 3.59255299e-02 3.34065974e-01 4.31770496e-02 -5.10988235e-01 5.67587912e-01 -2.32977066e-02 -1.71568084e+00 6.10277057e-01 1.12776555e-01 5.55066288e-01 -2.36192614e-01 1.29085720e+00 3.44979554e-01 9.24702585e-01 -5.63630939e-01 -2.11560845e-01 4.86814171e-01 -1.07475626e+00 -6.24858260e-01 -5.38381994e-01 -1.44089341e-01 -1.87711731e-01 -7.98468113e-01 1.20155847e+00 7.53056228e-01 2.67402172e-01 6.48529887e-01 -6.92719698e-01 -5.26609361e-01 1.18622589e+00 4.15006608e-01 -1.55264467e-01 7.46003926e-01 3.69199723e-01 1.54216027e+00 -3.27785224e-01 7.74854660e-01 7.93991923e-01 6.32290661e-01 7.17260182e-01 -1.48324442e+00 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a1a2a3c8-2456-4fbe-83a5-d8dc1441cd29	autolycus-exploiting-explainable-ai-xai-for	2302.02162	null	https://arxiv.org/abs/2302.02162v2	https://arxiv.org/pdf/2302.02162v2.pdf	AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against White-Box Models	Explainable Artificial Intelligence (XAI) encompasses a range of techniques and procedures aimed at elucidating the decision-making processes of AI models. While XAI is valuable in understanding the reasoning behind AI models, the data used for such revelations poses potential security and privacy vulnerabilities. Existing literature has identified privacy risks targeting machine learning models, including membership inference, model inversion, and model extraction attacks. Depending on the settings and parties involved, such attacks may target either the model itself or the training data used to create the model. We have identified that tools providing XAI can particularly increase the vulnerability of model extraction attacks, which can be a significant issue when the owner of an AI model prefers to provide only black-box access rather than sharing the model parameters and architecture with other parties. To explore this privacy risk, we propose AUTOLYCUS, a model extraction attack that leverages the explanations provided by popular explainable AI tools. We particularly focus on white-box machine learning (ML) models such as decision trees and logistic regression models. We have evaluated the performance of AUTOLYCUS on 5 machine learning datasets, in terms of the surrogate model's accuracy and its similarity to the target model. We observe that the proposed attack is highly effective; it requires up to 60x fewer queries to the target model compared to the state-of-the-art attack, while providing comparable accuracy and similarity. We first validate the performance of the proposed algorithm on decision trees, and then show its performance on logistic regression models as an indicator that the proposed algorithm performs well on white-box ML models in general. Finally, we show that the existing countermeasures remain ineffective for the proposed attack.	['Erman Ayday', 'Anisa Halimi', 'Abdullah Caglar Oksuz']	2023-02-04	null	null	null	null	['inference-attack', 'membership-inference-attack']	['adversarial', 'computer-vision']	[ 4.39113647e-01 7.41196990e-01 -3.96026522e-01 -3.59127522e-01 -6.65929019e-01 -1.15585041e+00 7.64455974e-01 1.47939295e-01 8.64166543e-02 4.77742255e-01 -3.76435846e-01 -1.04749680e+00 -2.07204923e-01 -7.56063461e-01 -9.88452852e-01 -4.29794461e-01 -7.92094991e-02 5.26252389e-01 -3.90446275e-01 5.59403062e-01 2.02503055e-01 5.94826221e-01 -9.82290089e-01 2.53516942e-01 6.74215853e-01 1.12871993e+00 -9.35624719e-01 5.70363283e-01 2.19669357e-01 7.29573607e-01 -7.43220031e-01 -1.13686967e+00 7.48731136e-01 -4.12510373e-02 -7.47144043e-01 -5.35527825e-01 4.77565080e-01 -5.84568024e-01 -2.01440617e-01 1.27034056e+00 -2.70819753e-01 -6.63354576e-01 6.22655451e-01 -2.30403924e+00 -3.10950816e-01 1.07233751e+00 -4.21754986e-01 -3.62731427e-01 1.06127234e-02 4.37207252e-01 8.38881433e-01 -2.71641523e-01 3.98444653e-01 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17838e90-78e1-4721-840c-ddc282167fb8	correcting-comma-errors-in-learner-essays-and	null	null	https://aclanthology.org/N12-1029	https://aclanthology.org/N12-1029.pdf	Correcting Comma Errors in Learner Essays, and Restoring Commas in Newswire Text	null	['Martin Chodorow', 'Joel Tetreault', 'Ross Israel']	2012-06-01	null	null	null	naacl-2012-6	['grammatical-error-detection']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 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2bceab27-7f67-4c32-83f5-db2483bc8240	a-privacy-preserving-content-based-image	2011.0027	null	https://arxiv.org/abs/2011.00270v1	https://arxiv.org/pdf/2011.00270v1.pdf	A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images	In this paper, we propose a novel content based-image retrieval scheme allowing the mixed use of encrypted and plain images for the first time. In the proposed scheme, images are encrypted by a block-scrambling method developed for encryption-then-compression (EtC) systems. The encrypted images, referred to as EtC images, can be compressed with JPEG, as well as for plain images. Image descriptors used for the proposed retrieval is designed to avoid the effect of image encryption. As a result, the use of EtC images and the descriptors allows us to carry out retrieval of both encrypted images and plain ones. In an experiment, the proposed scheme is demonstrated to have the same performance as conventional retrieval methods with plain images, even under the mixed use of plain images and EtC ones.	['Hitoshi Kiya', 'Kenta Iida']	2020-10-31	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[ 6.28964484e-01 -5.25315225e-01 1.93051472e-02 -2.11965919e-01 -5.21633863e-01 -5.03022492e-01 8.34372222e-01 2.60336578e-01 -9.28994894e-01 5.38512290e-01 -1.27155632e-01 3.34941037e-02 -2.27309451e-01 -1.10003722e+00 -2.94012517e-01 -9.04617667e-01 1.95250273e-01 -2.06933007e-01 9.44491699e-02 -1.34485856e-01 5.16616523e-01 4.90272909e-01 -1.89598489e+00 3.74044061e-01 6.52981460e-01 1.06752110e+00 6.02230191e-01 6.19598567e-01 -2.10873827e-01 6.33899331e-01 -6.32156372e-01 -6.25578225e-01 5.90807199e-01 -1.49510726e-01 -4.37034607e-01 -1.35618160e-02 8.10347423e-02 -8.63995910e-01 -7.84841776e-01 1.25342894e+00 4.17819053e-01 -1.88270643e-01 6.93668485e-01 -1.12152219e+00 -4.57245409e-01 1.64410099e-01 -3.88930708e-01 -1.04087636e-01 4.13234144e-01 -1.67528465e-01 5.93964159e-01 -6.62694454e-01 7.42712379e-01 9.20079410e-01 1.52114704e-01 2.08249778e-01 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3a80f81a-4a6e-49e1-ad07-afc96631bac5	an-adaptive-gmm-approach-to-background	1307.58	null	http://arxiv.org/abs/1307.5800v1	http://arxiv.org/pdf/1307.5800v1.pdf	An Adaptive GMM Approach to Background Subtraction for Application in Real Time Surveillance	Efficient security management has become an important parameter in todays world. As the problem is growing, there is an urgent need for the introduction of advanced technology and equipment to improve the state-of art of surveillance. In this paper we propose a model for real time background subtraction using AGMM. The proposed model is robust and adaptable to dynamic background, fast illumination changes, repetitive motion. Also we have incorporated a method for detecting shadows using the Horpresert color model. The proposed model can be employed for monitoring areas where movement or entry is highly restricted. So on detection of any unexpected events in the scene an alarm can be triggered and hence we can achieve real time surveillance even in the absence of constant human monitoring.	['Subra Mukherjee', 'Karen Das']	2013-07-22	null	null	null	null	['detecting-shadows']	['computer-vision']	[ 4.27714735e-01 -5.04221976e-01 3.78623337e-01 -1.03384189e-01 3.03776264e-01 -5.26488543e-01 6.06547177e-01 2.90042341e-01 -7.23233461e-01 7.39476264e-01 -3.28124672e-01 -3.51042241e-01 1.06600970e-01 -8.51155519e-01 -1.37282223e-01 -7.46836960e-01 1.30939439e-01 1.73310921e-01 1.03707409e+00 -3.36541027e-01 1.84984908e-01 9.73473966e-01 -1.50444913e+00 -1.52310520e-01 5.49610913e-01 7.00297594e-01 4.04389113e-01 1.09548819e+00 -4.91725691e-02 6.72981381e-01 -9.97606158e-01 1.51823297e-01 5.26365876e-01 -4.32381749e-01 -2.45245472e-01 4.07628089e-01 3.32941767e-03 -3.21686536e-01 1.06152251e-01 9.91251707e-01 3.75995159e-01 2.71575719e-01 3.41001064e-01 -8.92004788e-01 3.77308637e-01 -3.60559851e-01 -8.93041432e-01 6.12008929e-01 3.36279362e-01 1.23817891e-01 -2.07433835e-01 -2.30815887e-01 4.60376531e-01 8.71900082e-01 3.41454566e-01 3.73092115e-01 -8.28991830e-01 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1f335660-14b1-4c62-8535-f61cbf6f3936	semi-supervised-classification-for-dynamic	1704.05948	null	http://arxiv.org/abs/1704.05948v1	http://arxiv.org/pdf/1704.05948v1.pdf	Semi-supervised classification for dynamic Android malware detection	A growing number of threats to Android phones creates challenges for malware detection. Manually labeling the samples into benign or different malicious families requires tremendous human efforts, while it is comparably easy and cheap to obtain a large amount of unlabeled APKs from various sources. Moreover, the fast-paced evolution of Android malware continuously generates derivative malware families. These families often contain new signatures, which can escape detection when using static analysis. These practical challenges can also cause traditional supervised machine learning algorithms to degrade in performance. In this paper, we propose a framework that uses model-based semi-supervised (MBSS) classification scheme on the dynamic Android API call logs. The semi-supervised approach efficiently uses the labeled and unlabeled APKs to estimate a finite mixture model of Gaussian distributions via conditional expectation-maximization and efficiently detects malwares during out-of-sample testing. We compare MBSS with the popular malware detection classifiers such as support vector machine (SVM), $k$-nearest neighbor (kNN) and linear discriminant analysis (LDA). Under the ideal classification setting, MBSS has competitive performance with 98\% accuracy and very low false positive rate for in-sample classification. For out-of-sample testing, the out-of-sample test data exhibit similar behavior of retrieving phone information and sending to the network, compared with in-sample training set. When this similarity is strong, MBSS and SVM with linear kernel maintain 90\% detection rate while $k$NN and LDA suffer great performance degradation. When this similarity is slightly weaker, all classifiers degrade in performance, but MBSS still performs significantly better than other classifiers.	['Chih-Yuan Yang', 'Ravi Sahita', 'Mingwei Zhang', 'Li Chen']	2017-04-19	null	null	null	null	['android-malware-detection']	['miscellaneous']	[ 8.50041434e-02 -3.70217830e-01 -7.72036135e-01 -2.30263010e-01 -7.36254454e-01 -7.95440495e-01 4.96788353e-01 -1.34139001e-01 -5.18963560e-02 7.21465826e-01 -7.13182271e-01 -8.54614437e-01 1.72189310e-01 -6.85939252e-01 -5.54903507e-01 -6.35056496e-01 -1.87439889e-01 3.82062733e-01 6.80230379e-01 1.44894481e-01 1.77060768e-01 4.06970978e-01 -1.57102633e+00 2.37394631e-01 8.92205238e-01 1.11524880e+00 -1.27750114e-01 7.82585502e-01 -8.01442042e-02 5.20374477e-01 -7.74798632e-01 -5.74046552e-01 2.74532795e-01 -1.17835954e-01 -5.80522895e-01 -2.00253993e-01 -7.31608178e-03 -3.58840793e-01 1.62836909e-02 1.15630460e+00 8.15409422e-02 -3.55626106e-01 9.35486555e-01 -1.77714348e+00 -2.14207187e-01 1.73394814e-01 -7.61574745e-01 1.68102220e-01 4.40392017e-01 1.76262110e-01 4.98717189e-01 -6.09756589e-01 1.89837486e-01 9.25373256e-01 1.09276617e+00 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a4541dee-2b80-43b8-99cb-bea13c26230b	hipode-enhancing-offline-reinforcement	2306.06329	null	https://arxiv.org/abs/2306.06329v1	https://arxiv.org/pdf/2306.06329v1.pdf	HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach	Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data. To address the issue of limited data and improve downstream ORL performance, recent work has attempted to expand the dataset's coverage through data augmentation. However, most of these methods are tied to a specific policy (policy-dependent), where the generated data can only guarantee to support the current downstream ORL policy, limiting its usage scope on other downstream policies. Moreover, the quality of synthetic data is often not well-controlled, which limits the potential for further improving the downstream policy. To tackle these issues, we propose \textbf{HI}gh-quality \textbf{PO}licy-\textbf{DE}coupled~(HIPODE), a novel data augmentation method for ORL. On the one hand, HIPODE generates high-quality synthetic data by selecting states near the dataset distribution with potentially high value among candidate states using the negative sampling technique. On the other hand, HIPODE is policy-decoupled, thus can be used as a common plug-in method for any downstream ORL process. We conduct experiments on the widely studied TD3BC and CQL algorithms, and the results show that HIPODE outperforms the state-of-the-art policy-decoupled data augmentation method and most prevalent model-based ORL methods on D4RL benchmarks.	['Zhaopeng Meng', 'Yan Zheng', 'Jinyi Liu', 'Yi Ma', 'Shixi Lian']	2023-06-10	null	null	null	null	['d4rl']	['robots']	[-1.49672508e-01 5.51038049e-02 -1.05485058e+00 -9.29408967e-02 -8.16371441e-01 -4.57170069e-01 7.13407397e-01 3.37104172e-01 -5.87646842e-01 1.19997156e+00 2.49840692e-01 -7.96967149e-01 -7.76182413e-02 -8.66176963e-01 -5.66613495e-01 -8.96905303e-01 1.47072211e-01 7.13136017e-01 1.74405232e-01 -4.28169399e-01 4.65643406e-02 3.58765543e-01 -1.46386290e+00 -6.89791590e-02 1.10794127e+00 9.63994920e-01 9.07209143e-03 2.76863754e-01 -1.50769621e-01 7.38060057e-01 -7.92197049e-01 5.38847893e-02 4.65811610e-01 -4.98011231e-01 -5.80480516e-01 -3.39223593e-01 -1.06056638e-01 -4.73313004e-01 -5.30634820e-01 8.94537866e-01 7.25049078e-01 1.72104180e-01 3.22186321e-01 -1.43837357e+00 -2.59108067e-01 7.55472779e-01 -5.54311275e-01 3.45108449e-01 7.70193785e-02 6.13067508e-01 7.57509768e-01 -2.10263982e-01 4.45548564e-01 1.32036591e+00 2.32387170e-01 8.34521532e-01 -1.44596136e+00 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4.87066627e-01 2.20705450e-01 -8.42840672e-01 7.36921728e-02 -4.90429550e-01]	[4.064946174621582, 2.1746792793273926]
2367bd51-9fa8-4812-8411-173dc14670b5	uadam-unified-adam-type-algorithmic-framework	2305.05675	null	https://arxiv.org/abs/2305.05675v1	https://arxiv.org/pdf/2305.05675v1.pdf	UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization	Adam-type algorithms have become a preferred choice for optimisation in the deep learning setting, however, despite success, their convergence is still not well understood. To this end, we introduce a unified framework for Adam-type algorithms (called UAdam). This is equipped with a general form of the second-order moment, which makes it possible to include Adam and its variants as special cases, such as NAdam, AMSGrad, AdaBound, AdaFom, and Adan. This is supported by a rigorous convergence analysis of UAdam in the non-convex stochastic setting, showing that UAdam converges to the neighborhood of stationary points with the rate of $\mathcal{O}(1/T)$. Furthermore, the size of neighborhood decreases as $\beta$ increases. Importantly, our analysis only requires the first-order momentum factor to be close enough to 1, without any restrictions on the second-order momentum factor. Theoretical results also show that vanilla Adam can converge by selecting appropriate hyperparameters, which provides a theoretical guarantee for the analysis, applications, and further developments of the whole class of Adam-type algorithms.	['Danilo P. Mandic', 'Dongpo Xu', 'Jinlan Liu', 'Yiming Jiang']	2023-05-09	null	null	null	null	['stochastic-optimization', 'type']	['methodology', 'speech']	[-5.36787271e-01 1.24453388e-01 -9.57925245e-03 -1.39354527e-01 -6.46191955e-01 -3.44439417e-01 2.97890902e-01 3.07426274e-01 -7.74615049e-01 8.96889031e-01 -1.71031207e-01 -2.37255186e-01 -3.75802577e-01 -6.69554710e-01 -9.59917784e-01 -1.21909869e+00 -3.15155506e-01 4.12349552e-01 2.59751175e-02 -3.82804781e-01 9.62226316e-02 3.93308818e-01 -1.01333332e+00 -5.64822793e-01 1.10960436e+00 1.20262957e+00 -2.16479838e-01 5.56994975e-01 2.02487987e-02 3.34184557e-01 -2.97706038e-01 -6.17430925e-01 6.43046081e-01 -5.39328039e-01 -6.16223037e-01 -1.46357819e-01 1.90498233e-01 -2.76638091e-01 -1.83079004e-01 1.29452622e+00 5.09201229e-01 7.17823625e-01 3.79438460e-01 -1.08671832e+00 -1.45703882e-01 5.52520216e-01 -6.26255572e-01 2.88376987e-01 -2.66896188e-01 4.44298610e-02 1.07462943e+00 -9.15054858e-01 4.09942150e-01 9.07155097e-01 7.73683369e-01 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7.19333217e-02 4.69355404e-01 -6.32470489e-01 -7.76235461e-01 -4.74344790e-01]	[7.156538009643555, 4.0512261390686035]
2d7ae4c2-4cfd-4029-a68b-c9cb73feea6e	fusing-structure-from-motion-and-simulation	2304.0725	null	https://arxiv.org/abs/2304.07250v1	https://arxiv.org/pdf/2304.07250v1.pdf	Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments	The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advances in deep learning have enabled the localization using monocular visual cameras. While structure from motion (SfM) predicts the absolute pose from a point cloud, absolute pose regression (APR) methods learn a semantic understanding of the environment through neural networks. However, both fields face challenges caused by the environment such as motion blur, lighting changes, repetitive patterns, and feature-less structures. This study aims to address these challenges by incorporating additional information and regularizing the absolute pose using relative pose regression (RPR) methods. The optical flow between consecutive images is computed using the Lucas-Kanade algorithm, and the relative pose is predicted using an auxiliary small recurrent convolutional network. The fusion of absolute and relative poses is a complex task due to the mismatch between the global and local coordinate systems. State-of-the-art methods fusing absolute and relative poses use pose graph optimization (PGO) to regularize the absolute pose predictions using relative poses. In this work, we propose recurrent fusion networks to optimally align absolute and relative pose predictions to improve the absolute pose prediction. We evaluate eight different recurrent units and construct a simulation environment to pre-train the APR and RPR networks for better generalized training. Additionally, we record a large database of different scenarios in a challenging large-scale indoor environment that mimics a warehouse with transportation robots. We conduct hyperparameter searches and experiments to show the effectiveness of our recurrent fusion method compared to PGO.	['Christopher Mutschler', 'Bernd Bischl', 'David Rügamer', 'Lucas Heublein', 'Felix Ott']	2023-04-14	null	null	null	null	['pose-prediction']	['computer-vision']	[-3.88645977e-02 -3.83529186e-01 1.27600849e-01 -4.69610244e-01 -3.36591214e-01 -5.22441745e-01 6.91708267e-01 -2.33922809e-01 -3.67573589e-01 4.14930761e-01 2.06250343e-02 4.10531498e-02 -7.03267083e-02 -6.66676044e-01 -1.21733487e+00 -6.75898433e-01 -6.32373169e-02 4.51587975e-01 1.55020371e-01 -4.09414619e-01 1.95540145e-01 9.61118877e-01 -1.32564461e+00 -1.99846461e-01 6.17727637e-01 9.77837503e-01 6.00770414e-01 6.43101990e-01 7.35117868e-02 6.69709980e-01 -5.30310154e-01 1.13659218e-01 5.99890947e-01 -4.78198268e-02 -3.79101753e-01 1.12860635e-01 4.94032562e-01 -1.89816713e-01 -6.55234694e-01 9.80542719e-01 4.10641313e-01 4.18371230e-01 2.39631221e-01 -1.38441014e+00 -7.49786377e-01 1.09317645e-01 -4.46448416e-01 -1.68227091e-01 6.20021343e-01 2.98555374e-01 4.34102386e-01 -8.36721003e-01 7.23829806e-01 1.31957150e+00 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22a5f63c-b6f7-4aef-b4cc-8b5e62a9e1cf	graph-property-prediction-on-open-graph	2207.06027	null	https://arxiv.org/abs/2207.06027v1	https://arxiv.org/pdf/2207.06027v1.pdf	Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search	Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search). At the same time, we improve it based on the GNN topology design method F2GNN to further design the feature selection and fusion strategies, so as to further improve the performance of the model in the graph property prediction task while overcoming the over smoothing problem of deep GNN training. Finally, a performance breakthrough is achieved on these three datasets, which is significantly better than other methods with fixed aggregate function. It is proved that the NAS method has high generalization ability for multiple tasks and the advantage of our method in processing graph property prediction tasks.	['Quanming Yao', 'Lanning Wei', 'Huan Zhao', 'Xu Wang']	2022-07-13	null	null	null	null	['graph-property-prediction']	['graphs']	[ 6.92865774e-02 1.60911262e-01 -3.38145435e-01 -1.62016526e-01 1.11239001e-01 -1.62317380e-01 1.85763091e-01 4.25428480e-01 -6.62921891e-02 7.16112077e-01 -2.06199467e-01 -5.36836624e-01 -5.06406784e-01 -1.31852221e+00 -5.09603322e-01 -8.06643844e-01 -3.44099134e-01 2.93784529e-01 4.60763961e-01 -3.59227747e-01 -5.47638126e-02 7.40681648e-01 -9.53072727e-01 -6.91836551e-02 1.02734065e+00 1.32723534e+00 1.44765988e-01 2.86242425e-01 -7.06209168e-02 7.65638232e-01 -4.62591648e-01 -5.32925606e-01 1.91305667e-01 -6.60637766e-02 -6.77967250e-01 -2.00099409e-01 2.31776357e-01 2.90924579e-01 -8.20445955e-01 1.17564023e+00 6.72571540e-01 3.72376800e-01 5.49752116e-01 -1.19115400e+00 -8.83334935e-01 6.80956244e-01 -4.02508199e-01 1.63565829e-01 1.42816186e-01 4.20207083e-02 1.29294980e+00 -3.62175643e-01 5.84502280e-01 1.15470171e+00 7.33013630e-01 3.05905402e-01 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6aa45bcb-4aa8-416d-94ca-a9bd009de181	calibrated-predictive-distributions-via	2205.14568	null	https://arxiv.org/abs/2205.14568v3	https://arxiv.org/pdf/2205.14568v3.pdf	Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting	Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. Much research has been devoted to describing the predictive distribution (PD) $F(y\|\mathbf{x})$ of a target variable $y \in \mathbb{R}$ given complex input features $\mathbf{x} \in \mathcal{X}$. However, off-the-shelf PDs (from, e.g., normalizing flows and Bayesian neural networks) often lack conditional calibration with the probability of occurrence of an event given input $\mathbf{x}$ being significantly different from the predicted probability. Current calibration methods do not fully assess and enforce conditionally calibrated PDs. Here we propose \texttt{Cal-PIT}, a method that addresses both PD diagnostics and recalibration by learning a single probability-probability map from calibration data. The key idea is to regress probability integral transform scores against $\mathbf{x}$. The estimated regression provides interpretable diagnostics of conditional coverage across the feature space. The same regression function morphs the misspecified PD to a re-calibrated PD for all $\mathbf{x}$. We benchmark our corrected prediction bands (a by-product of corrected PDs) against oracle bands and state-of-the-art predictive inference algorithms for synthetic data. We also provide results for two applications: (i) probabilistic nowcasting given sequences of satellite images, and (ii) conditional density estimation of galaxy distances given imaging data (so-called photometric redshift estimation). Our code is available as a Python package https://github.com/lee-group-cmu/Cal-PIT .	['Ann B. Lee', 'Rafael Izbicki', 'Brett H. Andrews', 'Jeffrey A. Newman', 'David Zhao', 'Biprateep Dey']	2022-05-29	null	null	null	null	['photometric-redshift-estimation']	['miscellaneous']	[ 6.16093874e-02 3.87560017e-02 1.03929915e-01 -6.15599513e-01 -1.29792118e+00 -7.67619193e-01 6.77769363e-01 -1.38067126e-01 -1.50758311e-01 1.08338773e+00 -3.59318495e-01 -5.02914846e-01 -6.50281966e-01 -1.16936076e+00 -1.11104572e+00 -1.12162745e+00 -8.44692439e-02 9.88385975e-01 2.78182030e-01 3.46473932e-01 1.98482513e-01 4.44879502e-01 -1.62085342e+00 -3.27807575e-01 1.08673811e+00 1.30411649e+00 2.64668554e-01 8.14229667e-01 1.69639125e-01 5.02190173e-01 -5.16244054e-01 -8.57730269e-01 1.54808730e-01 -2.64987826e-01 -5.48501670e-01 -4.71319824e-01 3.13477874e-01 -1.24684595e-01 -4.40661252e-01 1.37249041e+00 1.45168453e-02 8.27627406e-02 1.23532999e+00 -1.33324564e+00 -5.85347652e-01 6.08665287e-01 -5.51949143e-01 3.38635951e-01 -1.46532074e-01 2.27576032e-01 1.19441307e+00 -6.35712326e-01 2.03128859e-01 1.01532590e+00 7.59491205e-01 -6.30238727e-02 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d76f8c1f-5e59-4bb1-b8e0-c217496af888	comparing-well-and-geophysical-data-for	null	null	https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR033045	https://www.researchgate.net/publication/364419032_Comparing_Well_and_Geophysical_Data_for_Temperature_Monitoring_within_a_Bayesian_Experimental_Design_Framework	Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework	Temperature logs are an important tool in the geothermal industry. Temperature measurements from boreholes are used for exploration, system design, and monitoring. The number of observations, however, is not always sufficient to fully determine the temperature field or explore the entire parameter space of interest. Drilling in the best locations is still difficult and expensive. It is therefore critical to optimize the number and location of boreholes. Due to its higher spatial resolution and lower cost, four-dimensional (4D) temperature field monitoring via time-lapse Electrical Resistivity Tomography has been investigated as a potential alternative. We use Bayesian Evidential Learning (BEL), a Monte Carlo-based training approach, to optimize the design of a 4D temperature field monitoring experiment. We demonstrate how BEL can take into account various data source combinations (temperature logs combined with geophysical data) in the Bayesian optimal experimental design (BOED). To determine the optimal data source combination, we use the Root Mean Squared Error of the predicted target in the low dimensional latent space where BEL is solving the prediction problem. The parameter estimates are accurate enough to use in BOED. Furthermore, the method is not limited to monitoring temperature fields and can be applied to other similar experimental design problems. The method is computationally efficient and requires little training data. For the considered optimal design problem, a training set of only 200 samples and a test set of 50 samples is sufficient.	['Thomas Hermans', 'Eric Laloy', 'Maximilian Ramgraber', 'Nolwenn Lesparre', 'Nicolas Compaire', 'Robin Thibaut']	2022-10-19	null	null	null	water-resources-research-2022-10	['time-series-regression']	['time-series']	[ 5.88244200e-03 -3.47261906e-01 -5.01320623e-02 -1.05787173e-01 -5.99108458e-01 -6.00414202e-02 5.13094008e-01 2.75831044e-01 -4.85317051e-01 9.74129856e-01 -2.58133084e-01 -5.38945436e-01 -6.03938520e-01 -7.63945103e-01 -3.56230021e-01 -1.11410952e+00 -2.14655846e-01 7.76418447e-01 1.88954145e-01 2.61459351e-01 5.06993890e-01 9.80239928e-01 -1.21519160e+00 -4.17354345e-01 9.78487253e-01 1.05205476e+00 6.98471427e-01 2.91907549e-01 2.29492888e-01 2.77381271e-01 -2.93316007e-01 6.20459378e-01 6.09781444e-02 -3.28414381e-01 -7.33215630e-01 6.64438158e-02 -4.70717400e-01 -3.83800209e-01 4.23279613e-01 6.63334310e-01 4.65613604e-01 5.02593637e-01 1.07455099e+00 -8.42925787e-01 3.64248425e-01 1.99535161e-01 -7.52786696e-01 1.28561482e-01 -2.80894578e-01 -2.54277885e-03 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1.22716665e+00 -2.02848047e-01 1.53768107e-01 1.01005876e+00 -1.04310584e+00 -6.22722745e-01 -1.19540811e+00 -1.23936236e+00 -3.73441845e-01 3.97763029e-02 -1.13627195e+00 -6.40556872e-01 -3.44975859e-01]	[6.3514485359191895, 3.401796817779541]
ca59358f-4c70-40bf-b08b-c6e08f64913f	learning-multimodal-graph-to-graph-1	1812.0107	null	http://arxiv.org/abs/1812.01070v3	http://arxiv.org/pdf/1812.01070v3.pdf	Learning Multimodal Graph-to-Graph Translation for Molecular Optimization	We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.	['Wengong Jin', 'Regina Barzilay', 'Kevin Yang', 'Tommi Jaakkola']	2018-12-03	null	null	null	null	['graph-to-graph-translation']	['graphs']	[ 6.73045456e-01 2.75560647e-01 -5.47006071e-01 -1.12806886e-01 -1.07046247e+00 -9.77311432e-01 6.57315433e-01 1.98470175e-01 -1.16531037e-01 1.26646376e+00 3.72336984e-01 -4.41164643e-01 3.94588828e-01 -7.94809759e-01 -1.45030451e+00 -8.12831998e-01 6.91675991e-02 8.55755806e-01 -2.49887511e-01 -3.55993718e-01 1.79034457e-01 4.95466232e-01 -3.05564523e-01 3.55576307e-01 1.04570270e+00 2.35822365e-01 -1.48304895e-01 9.80006218e-01 -3.74602191e-02 4.25177097e-01 -4.77953523e-01 -8.15392077e-01 2.55010188e-01 -8.48482609e-01 -5.86695075e-01 -2.58724153e-01 7.35228539e-01 1.75348178e-01 -3.68631035e-01 1.01156843e+00 7.37023354e-01 -2.13736463e-02 1.18197596e+00 -8.42769802e-01 -1.16925633e+00 6.29028440e-01 -2.43285149e-01 -8.14725012e-02 3.95581096e-01 5.61124265e-01 1.25942838e+00 -6.29927039e-01 1.20559919e+00 1.23686874e+00 1.67923391e-01 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d7cb76ef-f04e-479e-966c-98a5c8195a4a	towards-multilingual-conversations-in-the	null	null	https://aclanthology.org/L14-1556	https://aclanthology.org/L14-1556.pdf	Towards Multilingual Conversations in the Medical Domain: Development of Multilingual Medical Data and A Network-based ASR System	This paper outlines the recent development on multilingual medical data and multilingual speech recognition system for network-based speech-to-speech translation in the medical domain. The overall speech-to-speech translation (S2ST) system was designed to translate spoken utterances from a given source language into a target language in order to facilitate multilingual conversations and reduce the problems caused by language barriers in medical situations. Our final system utilizes a weighted finite-state transducers with n-gram language models. Currently, the system successfully covers three languages: Japanese, English, and Chinese. The difficulties involved in connecting Japanese, English and Chinese speech recognition systems through Web servers will be discussed, and the experimental results in simulated medical conversation will also be presented.	['Ryosuke Isotani', 'Keigo Kubo', 'Fumihiro Adachi', 'Tomoki Toda', 'Sho Matsumiya', 'Satoshi Nakamura', 'Sakriani Sakti', 'Graham Neubig']	2014-05-01	null	null	null	lrec-2014-5	['speech-to-speech-translation']	['speech']	[ 1.98806763e-01 4.15039152e-01 -2.41236404e-01 -4.77735668e-01 -1.47779822e+00 -4.27951515e-02 3.41494054e-01 -5.61278947e-02 -4.15084988e-01 9.57719743e-01 5.84349990e-01 -1.06500936e+00 4.66464162e-01 -2.36131102e-01 -3.24063841e-03 -5.34093678e-01 6.82330355e-02 7.93199360e-01 2.54126370e-01 -4.28177863e-01 -3.73179525e-01 3.40289325e-01 -2.72638351e-01 5.87886810e-01 7.49336064e-01 2.10746750e-03 6.27830923e-01 1.12508512e+00 -6.15923643e-01 8.39010000e-01 -8.10367465e-01 -2.00483173e-01 -2.53703415e-01 -9.12274361e-01 -1.20482254e+00 2.07252637e-01 -4.62117523e-01 -8.23719576e-02 -3.99501890e-01 9.06085849e-01 8.84162724e-01 -2.79666781e-01 2.34197408e-01 -5.45470059e-01 -3.10222715e-01 9.24523711e-01 1.94052830e-01 3.94901216e-01 7.33469665e-01 -1.44836619e-01 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220312cf-c24a-477e-854d-37375d8cc802	phase-aware-single-stage-speech-denoising-and-1	2006.00687	null	https://arxiv.org/abs/2006.00687v1	https://arxiv.org/pdf/2006.00687v1.pdf	Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net	In this work, we tackle a denoising and dereverberation problem with a single-stage framework. Although denoising and dereverberation may be considered two separate challenging tasks, and thus, two modules are typically required for each task, we show that a single deep network can be shared to solve the two problems. To this end, we propose a new masking method called phase-aware beta-sigmoid mask (PHM), which reuses the estimated magnitude values to estimate the clean phase by respecting the triangle inequality in the complex domain between three signal components such as mixture, source and the rest. Two PHMs are used to deal with direct and reverberant source, which allows controlling the proportion of reverberation in the enhanced speech at inference time. In addition, to improve the speech enhancement performance, we propose a new time-domain loss function and show a reasonable performance gain compared to MSE loss in the complex domain. Finally, to achieve a real-time inference, an optimization strategy for U-Net is proposed which significantly reduces the computational overhead up to 88.9% compared to the na\"ive version.	[]	2020-06-01	phase-aware-single-stage-speech-denoising-and	https://arxiv.org/abs/2006.00687	https://arxiv.org/pdf/2006.00687	interspeech-2020-6	['speech-denoising']	['speech']	[ 1.81178555e-01 -2.08712861e-01 4.14085984e-01 -4.05556887e-01 -8.78496051e-01 -1.87959284e-01 1.31978512e-01 -2.32407048e-01 -5.03595352e-01 7.21190393e-01 2.07461603e-02 -2.39043072e-01 -1.47625450e-02 -6.20270312e-01 -6.64333463e-01 -1.08457232e+00 7.32758269e-02 -3.86804521e-01 4.19507250e-02 -1.33237675e-01 -1.32150844e-01 1.35438755e-01 -1.32133114e+00 -1.14127446e-03 1.34607017e+00 1.27948666e+00 4.96673465e-01 4.84382421e-01 2.41504177e-01 5.00154793e-01 -9.65105951e-01 -1.79584950e-01 1.42088786e-01 -6.07024789e-01 5.89029714e-02 -1.24285989e-01 1.92565888e-01 -3.86211634e-01 -4.96185243e-01 1.41578948e+00 1.08477294e+00 3.58631372e-01 5.06787896e-01 -9.44461703e-01 -1.88894957e-01 6.44740939e-01 -7.57823229e-01 1.96197942e-01 -3.87862176e-02 1.23537362e-01 6.51472092e-01 -8.56027365e-01 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8.52095306e-01 -8.94864559e-01 -7.59695530e-01 -1.14665329e+00 -9.40839767e-01 4.64615598e-02 6.56674802e-01 -4.52373415e-01 -5.93221486e-01 -1.36106625e-01]	[14.966816902160645, 5.9065070152282715]
e3aeb223-bb5e-45ad-948b-0a554732a4ae	the-ethical-ambiguity-of-ai-data-enrichment	2306.018	null	https://arxiv.org/abs/2306.01800v1	https://arxiv.org/pdf/2306.01800v1.pdf	The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices	The technical progression of artificial intelligence (AI) research has been built on breakthroughs in fields such as computer science, statistics, and mathematics. However, in the past decade AI researchers have increasingly looked to the social sciences, turning to human interactions to solve the challenges of model development. Paying crowdsourcing workers to generate or curate data, or data enrichment, has become indispensable for many areas of AI research, from natural language processing to reinforcement learning from human feedback (RLHF). Other fields that routinely interact with crowdsourcing workers, such as Psychology, have developed common governance requirements and norms to ensure research is undertaken ethically. This study explores how, and to what extent, comparable research ethics requirements and norms have developed for AI research and data enrichment. We focus on the approach taken by two leading conferences: ICLR and NeurIPS, and journal publisher Springer. In a longitudinal study of accepted papers, and via a comparison with Psychology and CHI papers, this work finds that leading AI venues have begun to establish protocols for human data collection, but these are are inconsistently followed by authors. Whilst Psychology papers engaging with crowdsourcing workers frequently disclose ethics reviews, payment data, demographic data and other information, similar disclosures are far less common in leading AI venues despite similar guidance. The work concludes with hypotheses to explain these gaps in research ethics practices and considerations for its implications.	['Brent Mittelstadt', 'Will Hawkins']	2023-06-01	null	null	null	null	['ethics']	['miscellaneous']	[ 2.16826145e-02 5.37242055e-01 -1.15169346e-01 -5.40316582e-01 -5.62165916e-01 -6.84326053e-01 5.15311062e-01 5.75923443e-01 -9.63721395e-01 7.44489014e-01 6.84564173e-01 -4.77114290e-01 -1.34015922e-02 -1.61740094e-01 -4.25328851e-01 -2.43574470e-01 4.76538777e-01 4.49687243e-01 -1.60224006e-01 -1.24966964e-01 7.38928258e-01 3.56156319e-01 -1.40553463e+00 -1.03390433e-01 8.80596399e-01 1.80046469e-01 -3.91393125e-01 5.13320744e-01 -6.84425831e-02 1.05664563e+00 -7.28217185e-01 -8.13349128e-01 3.71506542e-01 -3.49676907e-01 -8.63839328e-01 -1.26283482e-01 2.65486211e-01 -4.56096977e-01 8.12826976e-02 7.37208426e-01 7.52948403e-01 1.17816009e-01 2.18013465e-01 -1.54835403e+00 -1.14009583e+00 4.63002414e-01 -5.85393310e-01 2.11159006e-01 6.91262841e-01 5.90913951e-01 6.64305389e-01 -5.40002108e-01 7.84763992e-01 1.36603642e+00 5.67076385e-01 5.09585798e-01 -1.02972460e+00 -7.50207365e-01 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2.15740558e-02 -2.84753054e-01 -3.98065537e-01 -7.32398257e-02]	[9.288463592529297, 6.539114475250244]
10fa5ad9-fc1e-46fc-80ef-c03c70fe1aa3	event-camera-based-visual-odometry-for	2305.08962	null	https://arxiv.org/abs/2305.08962v1	https://arxiv.org/pdf/2305.08962v1.pdf	Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface	Our paper proposes a direct sparse visual odometry method that combines event and RGB-D data to estimate the pose of agile-legged robots during dynamic locomotion and acrobatic behaviors. Event cameras offer high temporal resolution and dynamic range, which can eliminate the issue of blurred RGB images during fast movements. This unique strength holds a potential for accurate pose estimation of agile-legged robots, which has been a challenging problem to tackle. Our framework leverages the benefits of both RGB-D and event cameras to achieve robust and accurate pose estimation, even during dynamic maneuvers such as jumping and landing a quadruped robot, the Mini-Cheetah. Our major contributions are threefold: Firstly, we introduce an adaptive time surface (ATS) method that addresses the whiteout and blackout issue in conventional time surfaces by formulating pixel-wise decay rates based on scene complexity and motion speed. Secondly, we develop an effective pixel selection method that directly samples from event data and applies sample filtering through ATS, enabling us to pick pixels on distinct features. Lastly, we propose a nonlinear pose optimization formula that simultaneously performs 3D-2D alignment on both RGB-based and event-based maps and images, allowing the algorithm to fully exploit the benefits of both data streams. We extensively evaluate the performance of our framework on both public datasets and our own quadruped robot dataset, demonstrating its effectiveness in accurately estimating the pose of agile robots during dynamic movements.	['Donghyun Kim', 'Erik Learned-Miller', 'Michael Yang', 'Zhipeng Tang', 'Shifan Zhu']	2023-05-15	null	null	null	null	['visual-odometry']	['robots']	[ 1.46916822e-01 -3.52068841e-01 1.73312187e-01 -9.73848850e-02 -6.00801945e-01 -5.84965765e-01 1.61498576e-01 -1.11114308e-01 -6.79783642e-01 5.65444350e-01 -1.93011656e-01 3.13316166e-01 3.78820696e-03 -9.08708513e-01 -9.59829628e-01 -5.24001598e-01 -4.97062296e-01 5.46090066e-01 6.47073984e-01 -5.03650367e-01 1.64326325e-01 5.19231021e-01 -1.86837196e+00 -3.63528162e-01 7.57971823e-01 1.02004325e+00 2.70277083e-01 6.77250803e-01 4.34577018e-01 4.21773016e-01 -3.75648260e-01 -5.79501539e-02 7.05744565e-01 -2.41579086e-01 -2.18849555e-01 2.77653128e-01 4.52674776e-01 -4.49588895e-01 -3.39266926e-01 6.15145326e-01 6.48384750e-01 2.59292454e-01 3.22552860e-01 -1.30490899e+00 5.27191982e-02 -1.50515184e-01 -8.73873413e-01 4.21517231e-02 5.68404257e-01 8.08519185e-01 7.03792453e-01 -7.90610611e-01 7.53577113e-01 1.04420233e+00 1.00895870e+00 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-4.76972640e-01 -6.48497224e-01 3.68461847e-01 4.44296151e-01 -1.09641683e+00 -2.50774324e-01 1.10760912e-01]	[7.547848224639893, -1.6077483892440796]
59c1c474-567c-4898-8adb-8e144d45ab61	approximately-optimal-domain-adaptation-with	2302.14186	null	https://arxiv.org/abs/2302.14186v2	https://arxiv.org/pdf/2302.14186v2.pdf	Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis	We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average hypothesis representing previously seen source tasks and ii) a hypothesis trained on a new target task. For a particular generative setting we derive the optimal convex combination of the two models under 0-1 loss, propose a computable approximation, and study the effect of various parameter settings on the relative risks between the optimal hypothesis, hypothesis i), and hypothesis ii). We demonstrate the effectiveness of the proposed optimal classifier in the context of EEG- and ECG-based classification settings and argue that the optimal classifier can be computed without access to direct information from any of the individual source tasks. We conclude by discussing further applications, limitations, and possible future directions.	['Carey E. Priebe', 'Joshua T. Vogelstein', 'Ashwin De Silva', 'Weiwei Yang', 'Hayden S. Helm']	2023-02-27	null	null	null	null	['eeg', 'eeg']	['methodology', 'time-series']	[ 5.84522545e-01 3.11899602e-01 -1.84546784e-01 -5.38380682e-01 -1.10445511e+00 -4.12270010e-01 5.99926412e-01 1.98062181e-01 -6.16110206e-01 9.33203161e-01 1.31205752e-01 -1.78699911e-01 -4.78171080e-01 -2.18626097e-01 -5.79950869e-01 -8.65626276e-01 -4.19288129e-01 3.97853732e-01 1.34849206e-01 3.07019591e-01 1.69498906e-01 3.53439748e-01 -1.13819611e+00 5.74768372e-02 9.70361233e-01 1.24789906e+00 1.35635644e-01 3.70842218e-01 5.56328475e-01 3.46575789e-02 -9.61485803e-01 -3.09152216e-01 5.25669754e-01 -7.93202996e-01 -4.52552021e-01 -6.42090512e-04 1.13188669e-01 -7.31728524e-02 -1.36222020e-01 9.92890894e-01 8.74620557e-01 2.16650322e-01 1.10791171e+00 -1.42712772e+00 -2.86576331e-01 5.61195016e-01 -3.11480403e-01 5.79502881e-01 6.22954071e-02 -7.13645369e-02 6.73548102e-01 -6.52007639e-01 3.97203356e-01 8.55130017e-01 6.50047362e-01 5.56883931e-01 -1.44742024e+00 -8.36871862e-01 2.43747965e-01 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90c2a33c-89f4-4524-9b07-7c32e9294b89	weakly-supervised-domain-adaptive-semantic	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Das_Weakly-Supervised_Domain_Adaptive_Semantic_Segmentation_With_Prototypical_Contrastive_Learning_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Das_Weakly-Supervised_Domain_Adaptive_Semantic_Segmentation_With_Prototypical_Contrastive_Learning_CVPR_2023_paper.pdf	Weakly-Supervised Domain Adaptive Semantic Segmentation With Prototypical Contrastive Learning	There has been a lot of effort in improving the performance of unsupervised domain adaptation for semantic segmentation task, however there is still a huge gap in performance when compared with supervised learning. In this work, we propose a common framework to use different weak labels, e.g. image, point and coarse labels from target domain to reduce this performance gap. Specifically, we propose to learn better prototypes that are representative class features, by exploiting these weak labels. We use these improved prototypes for contrastive alignment of class features. In particular, we perform two different feature alignments, first, we align pixel features with prototypes within each domain and second, we align pixel features from source to prototype of target domain in an asymmetric way. This asymmetric alignment is beneficial as it preserves the target features during training, which is essential when weak labels are available from target domain. Our experiments on standard benchmarks shows that our framework achieves significant improvement compared to existing works and is able to reduce the performance gap with supervised learning.	['Bernt Schiele', 'Dengxin Dai', 'Yongqin Xian', 'Anurag Das']	2023-01-01	null	null	null	cvpr-2023-1	['unsupervised-domain-adaptation']	['methodology']	[ 4.53990102e-01 2.18450144e-01 -3.54524851e-01 -6.07236981e-01 -5.48890173e-01 -5.10545671e-01 5.88051617e-01 2.38350719e-01 -6.10789835e-01 6.05629265e-01 2.30261367e-02 1.99699402e-01 -7.38616809e-02 -7.88813591e-01 -5.91737628e-01 -7.46837676e-01 3.83891642e-01 5.01163304e-01 9.04620826e-01 1.76804569e-02 5.02776206e-01 4.20736909e-01 -1.71016288e+00 2.78002292e-01 9.64975834e-01 8.16938162e-01 4.35802877e-01 1.34560913e-01 -5.45050919e-01 4.53229070e-01 -4.99124944e-01 -1.07278891e-01 3.15437317e-01 -4.27769065e-01 -1.20750701e+00 4.02671039e-01 4.50156868e-01 2.10566506e-01 3.35164309e-01 1.16549563e+00 3.66597176e-01 -1.80315133e-02 8.26916337e-01 -1.11861825e+00 -1.49881899e-01 5.23790240e-01 -7.17281997e-01 -1.51992738e-02 1.09189311e-02 -8.30919072e-02 1.00054157e+00 -5.96050978e-01 8.35637033e-01 1.09078133e+00 7.36670911e-01 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63ca721a-a4bb-4b7f-8586-7451c80859af	probabilistic-attention-based-on-gaussian	2302.04061	null	https://arxiv.org/abs/2302.04061v1	https://arxiv.org/pdf/2302.04061v1.pdf	Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning	Multiple Instance Learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised methods. This is especially interesting for areas where the creation of large annotated datasets remains challenging, as in medicine. Although recent deep learning MIL approaches have obtained state-of-the-art results, they are fully deterministic and do not provide uncertainty estimations for the predictions. In this work, we introduce the Attention Gaussian Process (AGP) model, a novel probabilistic attention mechanism based on Gaussian Processes for deep MIL. AGP provides accurate bag-level predictions as well as instance-level explainability, and can be trained end-to-end. Moreover, its probabilistic nature guarantees robustness to overfitting on small datasets and uncertainty estimations for the predictions. The latter is especially important in medical applications, where decisions have a direct impact on the patient's health. The proposed model is validated experimentally as follows. First, its behavior is illustrated in two synthetic MIL experiments based on the well-known MNIST and CIFAR-10 datasets, respectively. Then, it is evaluated in three different real-world cancer detection experiments. AGP outperforms state-of-the-art MIL approaches, including deterministic deep learning ones. It shows a strong performance even on a small dataset with less than 100 labels and generalizes better than competing methods on an external test set. Moreover, we experimentally show that predictive uncertainty correlates with the risk of wrong predictions, and therefore it is a good indicator of reliability in practice. Our code is publicly available.	['Rafael Molina', 'Pablo Morales-Álvarez', 'Arne Schmidt']	2023-02-08	null	null	null	null	['multiple-instance-learning']	['methodology']	[ 6.22902177e-02 4.45915163e-01 -3.71264637e-01 -5.17072618e-01 -1.28573835e+00 -7.60256052e-02 5.51545143e-01 6.78794265e-01 -5.27208209e-01 1.15549374e+00 -8.15105885e-02 -1.61760643e-01 -3.65881205e-01 -6.83171988e-01 -9.25524890e-01 -1.08335114e+00 1.11647155e-02 1.11438227e+00 1.11834720e-01 3.72372955e-01 -1.25115976e-01 1.21822819e-01 -1.26168609e+00 2.32836455e-01 1.07472348e+00 1.19549930e+00 -8.11925903e-02 2.71120369e-01 7.32921585e-02 7.80659139e-01 -3.99009913e-01 -7.21693873e-01 -2.21801385e-01 -1.07206628e-02 -6.87295377e-01 -4.96704951e-02 1.03209512e-02 1.65915966e-01 8.88891444e-02 8.73317063e-01 4.39207613e-01 -2.59479764e-03 9.00157809e-01 -1.08739197e+00 -4.88227457e-01 7.29487121e-01 -3.62286329e-01 -2.48870999e-01 -2.06975639e-01 2.02997476e-01 1.16024423e+00 -6.72264993e-01 1.76446095e-01 1.09884667e+00 9.17003751e-01 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60691639-9620-489e-9103-59240f4fec21	prodmps-a-unified-perspective-on-dynamic-and	2210.01531	null	https://arxiv.org/abs/2210.01531v1	https://arxiv.org/pdf/2210.01531v1.pdf	ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives	Movement Primitives (MPs) are a well-known concept to represent and generate modular trajectories. MPs can be broadly categorized into two types: (a) dynamics-based approaches that generate smooth trajectories from any initial state, e. g., Dynamic Movement Primitives (DMPs), and (b) probabilistic approaches that capture higher-order statistics of the motion, e. g., Probabilistic Movement Primitives (ProMPs). To date, however, there is no method that unifies both, i. e. that can generate smooth trajectories from an arbitrary initial state while capturing higher-order statistics. In this paper, we introduce a unified perspective of both approaches by solving the ODE underlying the DMPs. We convert expensive online numerical integration of DMPs into basis functions that can be computed offline. These basis functions can be used to represent trajectories or trajectory distributions similar to ProMPs while maintaining all the properties of dynamical systems. Since we inherit the properties of both methodologies, we call our proposed model Probabilistic Dynamic Movement Primitives (ProDMPs). Additionally, we embed ProDMPs in deep neural network architecture and propose a new cost function for efficient end-to-end learning of higher-order trajectory statistics. To this end, we leverage Bayesian Aggregation for non-linear iterative conditioning on sensory inputs. Our proposed model achieves smooth trajectory generation, goal-attractor convergence, correlation analysis, non-linear conditioning, and online re-planing in one framework.	['Gerhard Neumann', 'Rudolf Lioutikov', 'Fabian Otto', 'Michael Volpp', 'Zeqi Jin', 'Ge Li']	2022-10-04	null	null	null	null	['numerical-integration']	['miscellaneous']	[-3.19933206e-01 -2.65709013e-01 -1.53270677e-01 5.68673527e-03 -8.65582347e-01 -6.54778957e-01 7.81097591e-01 1.34033978e-01 -2.87290394e-01 7.29428232e-01 2.66209602e-01 -2.04983532e-01 -5.35625160e-01 -9.27641869e-01 -9.55957532e-01 -8.89775097e-01 -2.81760633e-01 4.03541863e-01 3.06359947e-01 -1.10132515e-01 4.64362986e-02 7.52040625e-01 -1.49841356e+00 -6.15425482e-02 9.87084866e-01 8.70925307e-01 1.44141629e-01 6.21126056e-01 1.77580878e-01 5.81024110e-01 -1.32887200e-01 5.49112819e-02 1.33770302e-01 -3.56510878e-01 -4.97857839e-01 -3.66217613e-01 -1.08978778e-01 -4.06190306e-01 -5.80983102e-01 8.99131179e-01 3.69542599e-01 6.47088826e-01 7.73947060e-01 -1.48171937e+00 -4.00878489e-01 6.98067069e-01 -1.75504416e-01 -1.15587637e-01 1.85052693e-01 5.13292432e-01 8.21873128e-01 -5.54343522e-01 4.29961562e-01 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95c1f32e-5236-430c-ad38-84c8f5064cf7	cross-lingual-speaker-identification-from	null	null	https://openreview.net/forum?id=jCqESRWnumE	https://openreview.net/pdf?id=jCqESRWnumE	Cross-Lingual Speaker Identification from Weak Local Evidence	Speaker identification, determining which character said each utterance in text, benefits many downstream tasks. Most existing approaches use expert-defined rules or rule-based features to directly approach this task, but these approaches come with significant drawbacks, such as lack of contextual reasoning and poor cross-lingual generalization. In this work, we propose a speaker identification framework that addresses these issues. We first extract large-scale distant supervision signals in English via general-purpose tools and heuristics, and then apply these weakly-labeled instances with a focus on encouraging contextual reasoning to train a cross-lingual language model. We show that our final model outperforms the previous state-of-the-art methods on two English speaker identification benchmarks by $5.4\%$ in accuracy, as well as two Chinese speaker identification datasets by up to $4.7\%$.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['speaker-identification']	['speech']	[ 9.11092311e-02 -9.28579196e-02 -3.71749490e-01 -9.09647226e-01 -1.44279480e+00 -6.77720606e-01 5.12551129e-01 -1.60050154e-01 -4.04631406e-01 7.14155793e-01 2.52643615e-01 -5.49747109e-01 1.41443342e-01 -2.69969761e-01 -5.82048416e-01 -4.83422250e-01 1.87024698e-01 5.21982074e-01 1.28055200e-01 -1.41916901e-01 4.00838889e-02 2.03136891e-01 -1.31908751e+00 3.94132733e-01 1.05507612e+00 9.25407767e-01 -1.84621543e-01 3.73782575e-01 -3.34511548e-01 8.63220811e-01 -4.97095406e-01 -8.24238360e-01 -1.36036769e-01 -3.62731636e-01 -1.06474352e+00 -6.37063459e-02 4.78201479e-01 -1.42477930e-01 7.01589659e-02 1.21095240e+00 6.22027338e-01 1.66841540e-02 4.57084477e-01 -8.94769013e-01 -6.83546245e-01 1.23007214e+00 -4.85896885e-01 2.17267051e-01 4.59457427e-01 4.74108709e-03 1.11743665e+00 -9.70959783e-01 3.06861192e-01 1.39703226e+00 8.00124943e-01 8.74280512e-01 -1.32765722e+00 -8.93212616e-01 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f0885ad6-880c-45a3-95b0-59de99eadcec	prosit-latent-variable-discovery-with	2210.14763	null	https://arxiv.org/abs/2210.14763v1	https://arxiv.org/pdf/2210.14763v1.pdf	ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds	The most common ways to explore latent document dimensions are topic models and clustering methods. However, topic models have several drawbacks: e.g., they require us to choose the number of latent dimensions a priori, and the results are stochastic. Most clustering methods have the same issues and lack flexibility in various ways, such as not accounting for the influence of different topics on single documents, forcing word-descriptors to belong to a single topic (hard-clustering) or necessarily relying on word representations. We propose PROgressive SImilarity Thresholds - ProSiT, a deterministic and interpretable method, agnostic to the input format, that finds the optimal number of latent dimensions and only has two hyper-parameters, which can be set efficiently via grid search. We compare this method with a wide range of topic models and clustering methods on four benchmark data sets. In most setting, ProSiT matches or outperforms the other methods in terms six metrics of topic coherence and distinctiveness, producing replicable, deterministic results.	['Federico Bianchi', 'Dirk Hovy', 'Tommaso Fornaciari']	2022-10-26	null	null	null	null	['topic-models']	['natural-language-processing']	[-1.15713544e-01 -8.73775110e-02 -4.23370242e-01 -2.07350746e-01 -7.44016707e-01 -9.12381470e-01 9.83118594e-01 2.12703586e-01 -1.51726902e-01 2.36835539e-01 5.32267809e-01 -2.03719288e-01 -6.28843606e-01 -7.21382201e-01 3.41683701e-02 -1.01352870e+00 -1.46750525e-01 1.01998734e+00 4.57730889e-01 1.93600997e-01 7.59577334e-01 -3.08537250e-03 -1.50230145e+00 1.60287805e-02 9.22173798e-01 7.22243309e-01 2.11413741e-01 2.42447704e-01 -5.15404701e-01 -1.50135025e-01 -7.13037789e-01 -1.98774740e-01 3.85403857e-02 -3.52766871e-01 -7.08043933e-01 2.61420548e-01 6.59928620e-02 4.41782065e-02 1.57136679e-01 1.01035380e+00 2.81406939e-01 1.22904472e-01 1.14493334e+00 -1.33635318e+00 -4.76461709e-01 6.97517753e-01 -6.48942411e-01 7.90302604e-02 1.53080195e-01 -1.54175743e-01 1.18743575e+00 -8.34971905e-01 6.40757024e-01 1.57032514e+00 6.09359920e-01 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41bef87f-24d7-41a4-88b7-dfc07516681f	query-based-named-entity-recognition	1908.09138	null	https://arxiv.org/abs/1908.09138v2	https://arxiv.org/pdf/1908.09138v2.pdf	Query-Based Named Entity Recognition	In this paper, we propose a new strategy for the task of named entity recognition (NER). We cast the task as a query-based machine reading comprehension task: e.g., the task of extracting entities with PER is formalized as answering the question of "which person is mentioned in the text ?". Such a strategy comes with the advantage that it solves the long-standing issue of handling overlapping or nested entities (the same token that participates in more than one entity categories) with sequence-labeling techniques for NER. Additionally, since the query encodes informative prior knowledge, this strategy facilitates the process of entity extraction, leading to better performances. We experiment the proposed model on five widely used NER datasets on English and Chinese, including MSRA, Resume, OntoNotes, ACE04 and ACE05. The proposed model sets new SOTA results on all of these datasets.	['Zijun Sun', 'Yuxian Meng', 'Jiwei Li', 'Xiaoya Li']	2019-08-24	null	null	null	null	['entity-extraction']	['natural-language-processing']	[-3.38103212e-02 3.34948272e-01 1.50361940e-01 -3.73459458e-01 -8.76625121e-01 -7.32851326e-01 4.55790520e-01 5.95057964e-01 -1.07410502e+00 1.11748981e+00 5.66880584e-01 -3.25179875e-01 5.15917875e-02 -9.19039190e-01 -5.30078471e-01 -2.10363358e-01 2.84749717e-01 3.89840633e-01 2.38374770e-01 -1.85180560e-01 4.07348126e-01 2.96330124e-01 -1.22493732e+00 3.84687223e-02 1.29067528e+00 6.54715955e-01 3.45284790e-01 4.78302270e-01 -5.68789005e-01 8.70716989e-01 -7.19899714e-01 -7.73016453e-01 -3.69165719e-01 -1.77794114e-01 -1.49110830e+00 -1.56507209e-01 8.97645056e-02 1.40210986e-01 -1.12518862e-01 1.06746173e+00 5.19440293e-01 3.56268883e-01 7.19113827e-01 -8.30312133e-01 -5.85990012e-01 7.57510543e-01 -3.66088539e-01 3.70273530e-01 5.55569887e-01 -5.40065408e-01 1.16971302e+00 -7.70397663e-01 7.10451961e-01 8.78704369e-01 4.48048413e-01 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a3fae9d2-e89f-4eb4-aa3e-a17c70aa2964	joint-distribution-matters-deep-brownian	2204.04567	null	https://arxiv.org/abs/2204.04567v1	https://arxiv.org/pdf/2204.04567v1.pdf	Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification	Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically, this amounts to measure the dependency of image features, viewed as random vectors in a high-dimensional embedding space. Previous methods either only use marginal distributions without considering joint distributions, suffering from limited representation capability, or are computationally expensive though harnessing joint distributions. In this paper, we propose a deep Brownian Distance Covariance (DeepBDC) method for few-shot classification. The central idea of DeepBDC is to learn image representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. As the BDC metric is decoupled, we formulate it as a highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two different few-shot classification frameworks. We make experiments on six standard few-shot image benchmarks, covering general object recognition, fine-grained categorization and cross-domain classification. Extensive evaluations show our DeepBDC significantly outperforms the counterparts, while establishing new state-of-the-art results. The source code is available at http://www.peihuali.org/DeepBDC	['Peihua Li', 'Qilong Wang', 'Jiaming Lv', 'Fei Long', 'Jiangtao Xie']	2022-04-09	null	http://openaccess.thecvf.com//content/CVPR2022/html/Xie_Joint_Distribution_Matters_Deep_Brownian_Distance_Covariance_for_Few-Shot_Classification_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Xie_Joint_Distribution_Matters_Deep_Brownian_Distance_Covariance_for_Few-Shot_Classification_CVPR_2022_paper.pdf	cvpr-2022-1	['few-shot-image-classification']	['computer-vision']	[-8.15383065e-03 -3.63038838e-01 -2.32260004e-01 -5.48403144e-01 -8.27742994e-01 -1.86305359e-01 9.11366522e-01 1.14463851e-01 -4.45438862e-01 3.45094144e-01 -8.44154228e-03 1.84335053e-01 -2.63711363e-01 -8.47765386e-01 -6.99717581e-01 -7.81179547e-01 1.33968741e-01 2.00745597e-01 2.85647303e-01 -7.58925080e-02 2.04755768e-01 2.23333210e-01 -1.71607494e+00 1.42595112e-01 4.61209446e-01 1.41091287e+00 1.72823742e-01 5.59717655e-01 -9.64876041e-02 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55a3571f-deb5-4a62-a774-ed60287dc4d4	a-comparative-study-of-fruit-detection-and	1810.09499	null	http://arxiv.org/abs/1810.09499v2	http://arxiv.org/pdf/1810.09499v2.pdf	A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards	We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.	['Nicolai Häni', 'Volkan Isler', 'Pravakar Roy']	2018-10-22	null	null	null	null	['yield-mapping-in-apple-orchards']	['computer-vision']	[ 1.14324987e-01 -7.09635675e-01 -8.81746560e-02 4.40631062e-02 -3.19495142e-01 -8.88494134e-01 4.92394090e-01 4.61613983e-01 -5.59748411e-01 3.51987220e-02 -6.82514191e-01 -1.08524024e-01 2.50913441e-01 -1.16854012e+00 -6.42767370e-01 -6.46557570e-01 -1.15654700e-01 2.70939380e-01 4.12388474e-01 1.53222576e-01 4.54041988e-01 4.97352570e-01 -1.57974708e+00 3.49913478e-01 3.98168653e-01 1.15489924e+00 4.60117757e-01 1.27833676e+00 -3.90603006e-01 8.05181324e-01 -9.90433156e-01 -6.52362585e-01 1.69977546e-01 9.78998542e-02 -2.74231642e-01 -3.33871603e-01 9.53850806e-01 -1.01309967e+00 -1.00513976e-02 1.02727461e+00 3.56016874e-01 -7.32169807e-01 6.50973201e-01 -1.10260928e+00 -9.16739762e-01 8.65724564e-01 -1.03147006e+00 1.66994423e-01 1.12578936e-01 2.72179265e-02 7.05629230e-01 -5.58050990e-01 2.01781943e-01 9.30913031e-01 1.12169647e+00 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1c80341a-3629-4ee4-9ed3-edf93b0b47ef	semeval-2016-task-11-complex-word	null	null	https://aclanthology.org/S16-1085	https://aclanthology.org/S16-1085.pdf	SemEval 2016 Task 11: Complex Word Identification	null	['Lucia Specia', 'Gustavo Paetzold']	2016-06-01	null	null	null	semeval-2016-6	['complex-word-identification']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 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c6dba10b-6a15-448f-9ce8-f479c4f561dc	multi-level-sequence-gan-for-group-activity	1812.07124	null	http://arxiv.org/abs/1812.07124v1	http://arxiv.org/pdf/1812.07124v1.pdf	Multi-Level Sequence GAN for Group Activity Recognition	We propose a novel semi-supervised, Multi-Level Sequential Generative Adversarial Network (MLS-GAN) architecture for group activity recognition. In contrast to previous works which utilise manually annotated individual human action predictions, we allow the models to learn it's own internal representations to discover pertinent sub-activities that aid the final group activity recognition task. The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks. Action-based feature fusion is performed through novel gated fusion units that are able to consider long-term dependencies, exploring the relationships among all individual actions, to learn an intermediate representation or `action code' for the current group activity. The network achieves its semi-supervised behaviour by allowing it to perform group action classification together with the adversarial real/fake validation. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed architecture. Furthermore, we show that utilising both person-level and scene-level features facilitates the group activity prediction better than using only person-level features. Our proposed architecture outperforms current state-of-the-art results for sports and pedestrian based classification tasks on Volleyball and Collective Activity datasets, showing it's flexible nature for effective learning of group activities.	['Harshala Gammulle', 'Clinton Fookes', 'Sridha Sridharan', 'Simon Denman']	2018-12-18	null	null	null	null	['group-activity-recognition', 'activity-prediction', 'activity-prediction']	['computer-vision', 'computer-vision', 'time-series']	[ 6.14403665e-01 3.04376364e-01 -1.96454972e-01 -3.76571625e-01 -8.54197383e-01 -2.30342939e-01 1.10797858e+00 -2.08498091e-01 -4.07509238e-01 9.56665516e-01 4.82156098e-01 8.10618699e-02 1.62765294e-01 -8.49027038e-01 -9.32172060e-01 -8.84700537e-01 -4.66088951e-01 3.44576776e-01 4.08614635e-01 -2.23934293e-01 -1.54210538e-01 5.43795764e-01 -1.78935051e+00 7.91024983e-01 6.24889553e-01 1.01334596e+00 -3.24930280e-01 9.91748035e-01 4.30003703e-01 1.67499280e+00 -1.05762470e+00 -1.50230065e-01 2.20048711e-01 -1.06541491e+00 -7.70736456e-01 5.11687517e-01 4.22930479e-01 -1.76416188e-01 -3.64842474e-01 3.20580244e-01 3.77711743e-01 5.19187331e-01 5.86049259e-01 -1.42100978e+00 -3.68053764e-01 2.64432639e-01 5.58269992e-02 2.61372715e-01 8.45232785e-01 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2e3c723c-beb3-499e-8144-2c0be9a67744	complex-hyperbolic-knowledge-graph-embeddings	2211.03635	null	https://arxiv.org/abs/2211.03635v1	https://arxiv.org/pdf/2211.03635v1.pdf	Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform	The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to capture the hierarchical patterns due to its tree-like metrics, which addressed the limitations of the Euclidean embedding models. Recent explorations of the complex hyperbolic geometry further improved the hyperbolic embeddings for capturing a variety of hierarchical structures. However, the performance of the hyperbolic KG embedding models for non-transitive relations is still unpromising, while the complex hyperbolic embeddings do not deal with multi-relations. This paper aims to utilize the representation capacity of the complex hyperbolic geometry in multi-relational KG embeddings. To apply the geometric transformations which account for different relations and the attention mechanism in the complex hyperbolic space, we propose to use the fast Fourier transform (FFT) as the conversion between the real and complex hyperbolic space. Constructing the attention-based transformations in the complex space is very challenging, while the proposed Fourier transform-based complex hyperbolic approaches provide a simple and effective solution. Experimental results show that our methods outperform the baselines, including the Euclidean and the real hyperbolic embedding models.	['Simon See', 'Ginny Y. Wong', 'Yangqiu Song', 'Xin Liu', 'Huiru Xiao']	2022-11-07	null	null	null	null	['knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['graphs', 'methodology']	[-4.62709755e-01 3.68134081e-01 -3.47531997e-02 -6.98862374e-02 -2.18157753e-01 -4.50012863e-01 5.18730700e-01 1.94540530e-01 -3.00128251e-01 1.26671374e-01 5.42064607e-01 -3.80949706e-01 -6.91264212e-01 -1.17187333e+00 -4.25194591e-01 -8.35221708e-01 -4.33187753e-01 5.30674517e-01 3.16021711e-01 -3.88465583e-01 9.46033671e-02 5.63012779e-01 -1.37562680e+00 -4.05661799e-02 7.94667363e-01 7.57851720e-01 8.67424160e-02 5.92959762e-01 1.41037693e-02 5.88634610e-01 -3.18626434e-01 -6.02814853e-01 2.31081113e-01 -1.00634232e-01 -1.02709675e+00 -4.24146712e-01 2.72316545e-01 -3.13269049e-01 -9.90754426e-01 7.94132531e-01 5.40323079e-01 3.31872612e-01 6.77100539e-01 -1.60445845e+00 -1.33700454e+00 8.01679015e-01 -2.88954258e-01 1.50173858e-01 2.49179408e-01 -5.46076238e-01 1.63623130e+00 -1.07684684e+00 4.47998583e-01 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c670f79c-a806-4500-8c4f-34d7b80d64e6	domain-adaptation-for-semg-based-gesture	1901.06958	null	https://arxiv.org/abs/1901.06958v2	https://arxiv.org/pdf/1901.06958v2.pdf	Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks	Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and HighDensity (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.	['Krisztián Zsolt Varga', 'Ferenc Kovács', 'István Ketykó']	2019-01-21	null	null	null	null	['emg-gesture-recognition']	['medical']	[ 6.55820549e-01 -8.30210671e-02 -4.90243286e-01 -2.37557590e-01 -1.02841675e+00 -1.65748551e-01 1.53037101e-01 -9.13794816e-01 -6.13374531e-01 9.51155007e-01 4.42958444e-01 4.94314134e-01 -2.80179709e-01 -1.99551687e-01 -8.28565896e-01 -6.59466267e-01 -2.94045866e-01 2.47852132e-01 -1.40954882e-01 2.28924438e-01 2.07692862e-01 3.26946497e-01 -1.19963074e+00 4.57507342e-01 6.79308593e-01 1.15247452e+00 1.64589629e-01 4.64718729e-01 1.13990605e-01 3.52820978e-02 -7.67722011e-01 2.44611606e-01 5.33073187e-01 -7.01871514e-01 -5.05405486e-01 -1.61767229e-01 2.28396505e-01 -2.96418786e-01 -6.33160114e-01 9.91978407e-01 1.01466322e+00 1.57795370e-01 8.50135565e-01 -6.50098622e-01 -4.47167188e-01 3.96762371e-01 -6.55651569e-01 2.06402093e-01 6.24633312e-01 7.25814030e-02 3.48666400e-01 -7.18743205e-01 9.90102351e-01 7.28497326e-01 1.09871638e+00 1.11290896e+00 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-1.60981357e+00 8.59609067e-01 -1.63168192e-01 -7.79166698e-01 -2.88325250e-01 -1.34054273e-01]	[6.823986053466797, 0.15220455825328827]
938e713a-c2d6-4abc-80b6-7274f0de74f2	neural-separation-of-observed-and-unobserved	1811.12739	null	https://arxiv.org/abs/1811.12739v2	https://arxiv.org/pdf/1811.12739v2.pdf	Neural separation of observed and unobserved distributions	Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separation---to tackle the scenario of extracting a signal from an unobserved distribution additively mixed with a signal from an observed distribution. Our method iteratively learns to separate the known distribution from progressively finer estimates of the unknown distribution. In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce Latent Mixture Masking which ensures a good initialization. Extensive experiments on audio and image separation tasks show that our method outperforms current methods that use the same level of supervision, and often achieves similar performance to full supervision.	['Ariel Ephrat', 'Yedid Hoshen', 'Tavi Halperin']	2018-11-30	neural-separation-of-observed-and-unobserved-1	https://openreview.net/forum?id=SkelJnRqt7	https://openreview.net/pdf?id=SkelJnRqt7	iclr-2019-5	['speaker-separation']	['speech']	[ 4.45621461e-01 2.81863343e-02 -8.41121972e-02 -2.77419955e-01 -1.20872736e+00 -4.47951555e-01 4.82037157e-01 -2.72253633e-01 -1.96768135e-01 6.93020463e-01 2.57983580e-02 1.88311655e-02 -1.25031337e-01 -1.63119420e-01 -8.42197299e-01 -1.11839592e+00 -1.88411862e-01 4.86276478e-01 3.96271311e-02 1.81797102e-01 -2.48309508e-01 -8.69204625e-02 -1.45772266e+00 2.63459265e-01 7.69256115e-01 7.61101604e-01 1.62023306e-01 1.04224408e+00 -1.64597202e-02 8.25087845e-01 -9.86695707e-01 -3.19013447e-01 2.84675747e-01 -8.10850501e-01 -1.75110400e-01 4.24303859e-01 3.61299396e-01 -1.20776437e-01 4.45744209e-03 1.30089033e+00 5.89177310e-01 -9.53353494e-02 1.06384575e+00 -1.38331056e+00 -2.74681211e-01 9.75313425e-01 -9.23889339e-01 2.90098876e-01 7.36537650e-02 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fc8ae82c-b799-42c7-92fe-cacfd6505b10	emotion-aware-human-attention-prediction	null	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Cordel_Emotion-Aware_Human_Attention_Prediction_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Cordel_Emotion-Aware_Human_Attention_Prediction_CVPR_2019_paper.pdf	Emotion-Aware Human Attention Prediction	Despite the recent success in face recognition and object classification, in the field of human gaze prediction, computer models are still struggling to accurately mimic human attention. One main reason is that visual attention is a complex human behavior influenced by multiple factors, ranging from low-level features (e.g., color, contrast) to high-level human perception (e.g., objects interactions, object sentiment), making it difficult to model computationally. In this work, we investigate the relation between object sentiment and human attention. We first introduce a new evaluation metric (AttI) for measuring human attention that focuses on human fixation consensus. A series of empirical data analyses with AttI indicate that emotion-evoking objects receive attention favor, especially when they co-occur with emotionally-neutral objects, and this favor varies with different image complexity. Based on the empirical analyses, we design a deep neural network for human attention prediction which allows the attention bias on emotion-evoking objects to be encoded in its feature space. Experiments on two benchmark datasets demonstrate its superior performance, especially on metrics that evaluate relative importance of salient regions. This research provides the clearest picture to date on how object sentiments influence human attention, and it makes one of the first attempts to model this phenomenon computationally.	[' Mohan S. Kankanhalli', ' Zhiqi Shen', ' Shaojing Fan', 'Macario O. Cordel II']	2019-06-01	null	null	null	cvpr-2019-6	['eye-tracking']	['computer-vision']	[ 1.31454349e-01 -3.66322458e-01 -2.04207703e-01 -3.21346045e-01 4.50198740e-01 -1.18433222e-01 2.93789893e-01 8.65370557e-02 -3.69628489e-01 3.81478399e-01 2.08144054e-01 -7.71256015e-02 -1.42159150e-03 -3.53091806e-01 -5.62207222e-01 -6.36784971e-01 5.88439628e-02 -1.93533614e-01 8.13517645e-02 -1.90720588e-01 7.66554892e-01 4.78571117e-01 -2.07657743e+00 1.34802625e-01 5.65268397e-01 1.28704715e+00 2.93106139e-01 3.30190629e-01 -5.67185916e-02 8.84049952e-01 -6.17669940e-01 -4.70365882e-01 -5.09935915e-02 -7.77298272e-01 -5.45827985e-01 -4.34630103e-02 3.37562591e-01 -5.37394732e-03 4.68598120e-02 1.27723539e+00 6.07551575e-01 7.59049654e-02 8.57955277e-01 -1.70660210e+00 -1.39768028e+00 1.54864371e-01 -8.92740488e-01 7.45636523e-01 3.22305590e-01 3.50696892e-01 9.38244522e-01 -1.12826908e+00 2.07327232e-01 1.39468372e+00 2.06319153e-01 5.96031487e-01 -8.43682349e-01 -7.86971927e-01 2.31757462e-01 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769c6706-c2c3-4ecf-b6dd-da0b49ae446d	confident-anchor-induced-multi-source-free	null	null	http://proceedings.neurips.cc/paper/2021/hash/168908dd3227b8358eababa07fcaf091-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/168908dd3227b8358eababa07fcaf091-Paper.pdf	Confident Anchor-Induced Multi-Source Free Domain Adaptation	Unsupervised domain adaptation has attracted appealing academic attentions by transferring knowledge from labeled source domain to unlabeled target domain. However, most existing methods assume the source data are drawn from a single domain, which cannot be successfully applied to explore complementarily transferable knowledge from multiple source domains with large distribution discrepancies. Moreover, they require access to source data during training, which are inefficient and unpractical due to privacy preservation and memory storage. To address these challenges, we develop a novel Confident-Anchor-induced multi-source-free Domain Adaptation (CAiDA) model, which is a pioneer exploration of knowledge adaptation from multiple source domains to the unlabeled target domain without any source data, but with only pre-trained source models. Specifically, a source-specific transferable perception module is proposed to automatically quantify the contributions of the complementary knowledge transferred from multi-source domains to the target domain. To generate pseudo labels for the target domain without access to the source data, we develop a confident-anchor-induced pseudo label generator by constructing a confident anchor group and assigning each unconfident target sample with a semantic-nearest confident anchor. Furthermore, a class-relationship-aware consistency loss is proposed to preserve consistent inter-class relationships by aligning soft confusion matrices across domains. Theoretical analysis answers why multi-source domains are better than a single source domain, and establishes a novel learning bound to show the effectiveness of exploiting multi-source domains. Experiments on several representative datasets illustrate the superiority of our proposed CAiDA model. The code is available at https://github.com/Learning-group123/CAiDA.	['Tongliang Liu', 'Gan Sun', 'Anjin Liu', 'Zhen Fang', 'Jiahua Dong']	2021-12-01	null	https://openreview.net/forum?id=EAdJEN8xKUl	https://openreview.net/pdf?id=EAdJEN8xKUl	neurips-2021-12	['source-free-domain-adaptation']	['computer-vision']	[ 4.71417844e-01 2.12934762e-01 -5.45930445e-01 -5.37469864e-01 -1.15878630e+00 -8.76353145e-01 4.26590115e-01 1.36364549e-02 -1.16260670e-01 1.14837182e+00 -8.26419964e-02 4.92567662e-03 -1.19311221e-01 -6.34967148e-01 -9.63102579e-01 -7.68773675e-01 3.76027942e-01 5.56156278e-01 5.85936084e-02 2.25192383e-02 -9.51189026e-02 -8.77533108e-02 -1.05091417e+00 1.83925331e-01 1.39824355e+00 9.60793257e-01 1.78106248e-01 -1.02043785e-01 -1.58050865e-01 4.01217133e-01 -6.46812737e-01 -4.45039809e-01 5.09459913e-01 -6.77274346e-01 -6.56481802e-01 1.87213212e-01 1.73326984e-01 -1.68937489e-01 6.99307024e-02 1.36654460e+00 2.72631973e-01 4.67989743e-02 8.06310177e-01 -1.65983963e+00 -1.21959591e+00 3.47950399e-01 -4.95710969e-01 -2.63189912e-01 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fa15350a-cead-4436-afaf-dfb41e62aa87	adaptive-window-pruning-for-efficient-local	2306.14268	null	https://arxiv.org/abs/2306.14268v1	https://arxiv.org/pdf/2306.14268v1.pdf	Adaptive Window Pruning for Efficient Local Motion Deblurring	Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure. Existing image deblurring methods predominantly focus on global deblurring, inadvertently affecting the sharpness of backgrounds in locally blurred images and wasting unnecessary computation on sharp pixels, especially for high-resolution images. This paper aims to adaptively and efficiently restore high-resolution locally blurred images. We propose a local motion deblurring vision Transformer (LMD-ViT) built on adaptive window pruning Transformer blocks (AdaWPT). To focus deblurring on local regions and reduce computation, AdaWPT prunes unnecessary windows, only allowing the active windows to be involved in the deblurring processes. The pruning operation relies on the blurriness confidence predicted by a confidence predictor that is trained end-to-end using a reconstruction loss with Gumbel-Softmax re-parameterization and a pruning loss guided by annotated blur masks. Our method removes local motion blur effectively without distorting sharp regions, demonstrated by its exceptional perceptual and quantitative improvements (+0.24dB) compared to state-of-the-art methods. In addition, our approach substantially reduces FLOPs by 66% and achieves more than a twofold increase in inference speed compared to Transformer-based deblurring methods. We will make our code and annotated blur masks publicly available.	['Chen Change Loy', 'Chongyi Li', 'Huajun Feng', 'Shangchen Zhou', 'Jixin Zhao', 'Haoying Li']	2023-06-25	null	null	null	null	['deblurring']	['computer-vision']	[ 5.31876683e-01 -4.46077973e-01 1.80677608e-01 -1.63878635e-01 -6.52805030e-01 -3.82577568e-01 3.19141746e-01 -6.20938480e-01 -4.34712052e-01 7.56043375e-01 5.79882860e-01 -3.45768422e-01 2.30666772e-02 -2.83865809e-01 -6.94741011e-01 -7.87565231e-01 1.48827419e-01 -3.85148168e-01 4.48259413e-01 4.12835538e-01 4.15791184e-01 3.18417788e-01 -1.27669358e+00 2.85921097e-01 1.24663651e+00 9.60222960e-01 6.30338132e-01 1.05008829e+00 5.15814364e-01 1.10161638e+00 -5.91221094e-01 -2.73093075e-01 3.12745839e-01 -3.88895154e-01 -6.76440895e-01 1.57310084e-01 9.17978287e-01 -1.00538993e+00 -6.41045809e-01 1.27040672e+00 4.20397043e-01 2.92393625e-01 3.52287203e-01 -4.58478093e-01 -9.64630961e-01 2.16952577e-01 -1.00843632e+00 6.56761706e-01 3.29288431e-02 4.20138985e-01 4.77596432e-01 -9.30021942e-01 3.04681748e-01 1.11985290e+00 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0b018087-55a5-4cb6-a9c1-c06003020012	bebold-exploration-beyond-the-boundary-of-1	2012.08621	null	https://arxiv.org/abs/2012.08621v1	https://arxiv.org/pdf/2012.08621v1.pdf	BeBold: Exploration Beyond the Boundary of Explored Regions	Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR). There are many heuristics for IR, including visitation counts, curiosity, and state-difference. In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR. The criterion helps the agent explore Beyond the Boundary of explored regions and mitigates common issues in count-based methods, such as short-sightedness and detachment. The resulting method, BeBold, solves the 12 most challenging procedurally-generated tasks in MiniGrid with just 120M environment steps, without any curriculum learning. In comparison, the previous SoTA only solves 50% of the tasks. BeBold also achieves SoTA on multiple tasks in NetHack, a popular rogue-like game that contains more challenging procedurally-generated environments.	['Yuandong Tian', 'Joseph E. Gonzalez', 'Kurt Keutzer', 'Yi Wu', 'Xiaolong Wang', 'Huazhe Xu', 'Tianjun Zhang']	2020-12-15	bebold-exploration-beyond-the-boundary-of	https://openreview.net/forum?id=_ptUyYP19mP	https://openreview.net/pdf?id=_ptUyYP19mP	null	['nethack']	['playing-games']	[-2.77263612e-01 1.07550390e-01 -2.06648454e-01 5.05306870e-02 -7.38850057e-01 -7.42204249e-01 3.42303187e-01 1.30675241e-01 -8.22379887e-01 1.21003354e+00 1.75827872e-02 -4.85118747e-01 -4.05728400e-01 -7.27527857e-01 -7.44229555e-01 -7.86614060e-01 -4.46517289e-01 5.67798197e-01 1.38598472e-01 -7.25120008e-01 6.32113874e-01 2.10654780e-01 -1.49044335e+00 -3.00258458e-01 1.34482741e+00 7.46424079e-01 5.58257341e-01 4.46044207e-01 -9.81877744e-02 9.20823395e-01 -6.85543358e-01 2.55251974e-01 4.95784611e-01 -3.90722245e-01 -1.02306890e+00 -2.34536096e-01 -2.46550012e-02 -4.16507810e-01 -9.40622687e-02 7.82604277e-01 5.56433320e-01 7.31463373e-01 3.86542797e-01 -1.17710459e+00 -1.55301735e-01 9.92393017e-01 -9.98425364e-01 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333c61d5-be7a-4872-ba49-3b4211016af8	shiva-a-framework-for-graph-based-ontology	1403.7465	null	http://arxiv.org/abs/1403.7465v1	http://arxiv.org/pdf/1403.7465v1.pdf	Shiva: A Framework for Graph Based Ontology Matching	Since long, corporations are looking for knowledge sources which can provide structured description of data and can focus on meaning and shared understanding. Structures which can facilitate open world assumptions and can be flexible enough to incorporate and recognize more than one name for an entity. A source whose major purpose is to facilitate human communication and interoperability. Clearly, databases fail to provide these features and ontologies have emerged as an alternative choice, but corporations working on same domain tend to make different ontologies. The problem occurs when they want to share their data/knowledge. Thus we need tools to merge ontologies into one. This task is termed as ontology matching. This is an emerging area and still we have to go a long way in having an ideal matcher which can produce good results. In this paper we have shown a framework to matching ontologies using graphs.	['Nisheeth Joshi', 'Hemant Darbari', 'Iti Mathur', 'Ajai Kumar']	2014-03-28	null	null	null	null	['ontology-matching']	['knowledge-base']	[-3.88204038e-01 1.25242919e-01 -2.22858638e-01 -4.61944759e-01 -1.74109504e-01 -5.88432610e-01 7.31398880e-01 5.97393632e-01 -3.45489889e-01 5.39677858e-01 1.95706546e-01 -3.18938226e-01 -5.64013302e-01 -1.23807645e+00 -1.24788873e-01 -1.30578637e-01 1.61651582e-01 7.22686589e-01 5.85611582e-01 -6.00218952e-01 3.09066355e-01 5.22591531e-01 -1.70843399e+00 2.93343067e-01 8.47080827e-01 6.63978279e-01 3.42858434e-01 9.34558213e-02 -1.20759249e+00 7.33954072e-01 -4.44296718e-01 -7.33491063e-01 1.79064602e-01 -1.47903025e-01 -1.35568690e+00 -3.30027252e-01 5.84810413e-03 3.19344074e-01 3.66359413e-01 1.32478690e+00 2.43074104e-01 -1.86011419e-01 2.90731549e-01 -1.58410668e+00 -6.12910867e-01 6.81676388e-01 1.84928641e-01 -2.75297426e-02 7.69930005e-01 -5.40098071e-01 7.90772974e-01 -2.17870355e-01 1.01276934e+00 1.06944835e+00 6.31294549e-01 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24111f8f-63d5-4399-8e32-69fa41a8a9f1	omega-a-probabilistic-approach-to-referring	null	null	https://aclanthology.org/2020.inlg-1.36	https://aclanthology.org/2020.inlg-1.36.pdf	OMEGA : A probabilistic approach to referring expression generation in a virtual environment	In recent years, referring expression genera- tion algorithms were inspired by game theory and probability theory. In this paper, an al- gorithm is designed for the generation of re- ferring expressions (REG) that base on both models by integrating maximization of utilities into the content determination process. It im- plements cognitive models for assessing visual salience of objects and additional features. In order to evaluate the algorithm properly and validate the applicability of existing models and evaluative information criteria, both, pro- duction and comprehension studies, are con- ducted using a complex domain of objects, pro- viding new directions of approaching the eval- uation of REG algorithms.	['Maurice Langner']	null	null	null	null	inlg-acl-2020-12	['referring-expression-generation']	['computer-vision']	[ 2.42746070e-01 3.43829244e-01 -2.55387556e-02 -3.48352581e-01 -4.28775102e-01 -7.00900733e-01 5.84678888e-01 5.38468182e-01 -6.28095925e-01 7.38008142e-01 3.72973084e-01 -3.78624290e-01 -7.96440005e-01 -1.09058011e+00 6.48084730e-02 -1.28267799e-02 -2.88901199e-02 2.63707548e-01 -1.55864909e-01 -2.31230006e-01 7.79003799e-01 5.72845995e-01 -1.94151461e+00 1.97343796e-01 1.09040630e+00 9.80808198e-01 2.59635538e-01 5.01024365e-01 -2.95219928e-01 1.11553776e+00 -5.28608561e-01 -1.04377878e+00 -2.31381327e-01 -7.13556588e-01 -9.81468320e-01 1.97069153e-01 -1.27680540e-01 -2.42714033e-01 -2.61944413e-01 1.24121761e+00 3.57237548e-01 4.88221318e-01 9.03174996e-01 -1.30738258e+00 -1.01423872e+00 7.94244647e-01 6.82439506e-02 4.02759492e-01 9.64190602e-01 -7.41066337e-02 1.46470416e+00 -7.36501992e-01 8.81033659e-01 1.50806677e+00 1.50712073e-01 3.38186085e-01 -1.01632977e+00 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3.16625476e-01 -7.83043563e-01 -8.86118948e-01 -1.04992330e-01 -2.84329623e-01]	[9.429643630981445, 6.942612648010254]
5200fcb9-6ca3-49d0-a9e2-c59224fa7b34	tspnet-hierarchical-feature-learning-via	2010.05468	null	https://arxiv.org/abs/2010.05468v1	https://arxiv.org/pdf/2010.05468v1.pdf	TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation	Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to explicitly segmenting the videos into isolated signs. However, these methods neglect the temporal information of signs and lead to substantial ambiguity in translation. In this paper, we explore the temporal semantic structures of signvideos to learn more discriminative features. To this end, we first present a novel sign video segment representation which takes into account multiple temporal granularities, thus alleviating the need for accurate video segmentation. Taking advantage of the proposed segment representation, we develop a novel hierarchical sign video feature learning method via a temporal semantic pyramid network, called TSPNet. Specifically, TSPNet introduces an inter-scale attention to evaluate and enhance local semantic consistency of sign segments and an intra-scale attention to resolve semantic ambiguity by using non-local video context. Experiments show that our TSPNet outperforms the state-of-the-art with significant improvements on the BLEU score (from 9.58 to 13.41) and ROUGE score (from 31.80 to 34.96)on the largest commonly-used SLT dataset. Our implementation is available at https://github.com/verashira/TSPNet.	['Hongdong Li', 'Hanna Suominen', 'Ben Swift', 'Kaihao Zhang', 'Xin Yu', 'Chenchen Xu', 'Dongxu Li']	2020-10-12	null	http://proceedings.neurips.cc/paper/2020/hash/8c00dee24c9878fea090ed070b44f1ab-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/8c00dee24c9878fea090ed070b44f1ab-Paper.pdf	neurips-2020-12	['sign-language-translation']	['computer-vision']	[ 3.98427635e-01 -4.57475185e-01 -4.83121097e-01 -4.64827955e-01 -7.50546515e-01 -6.48902953e-01 4.64105994e-01 -5.34548998e-01 -4.65481728e-01 4.99078602e-01 4.98098582e-01 -9.47323143e-02 3.85721698e-02 -3.25667053e-01 -5.82633376e-01 -6.32057667e-01 9.59286094e-02 1.21576205e-01 5.76392889e-01 2.93513015e-02 2.29475215e-01 2.07058534e-01 -1.49579561e+00 4.42515880e-01 1.02228820e+00 8.51411343e-01 1.52623564e-01 4.86335427e-01 -2.88955271e-01 9.14540112e-01 -5.66684186e-01 -1.57250270e-01 2.90889859e-01 -1.01463497e+00 -7.01990485e-01 3.40756506e-01 6.72422945e-01 -6.46418691e-01 -4.87732202e-01 1.01472044e+00 4.17304873e-01 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d2e36998-9118-4b68-b279-ccb1f07deab6	multilayer-deep-feature-extraction-for-visual	2208.10044	null	https://arxiv.org/abs/2208.10044v1	https://arxiv.org/pdf/2208.10044v1.pdf	Multilayer deep feature extraction for visual texture recognition	Convolutional neural networks have shown successful results in image classification achieving real-time results superior to the human level. However, texture images still pose some challenge to these models due, for example, to the limited availability of data for training in several problems where these images appear, high inter-class similarity, the absence of a global viewpoint of the object represented, and others. In this context, the present paper is focused on improving the accuracy of convolutional neural networks in texture classification. This is done by extracting features from multiple convolutional layers of a pretrained neural network and aggregating such features using Fisher vector. The reason for using features from earlier convolutional layers is obtaining information that is less domain specific. We verify the effectiveness of our method on texture classification of benchmark datasets, as well as on a practical task of Brazilian plant species identification. In both scenarios, Fisher vectors calculated on multiple layers outperform state-of-art methods, confirming that early convolutional layers provide important information about the texture image for classification.	['Joao B. Florindo', 'Antonio Elias Fabris', 'Lucas O. Lyra']	2022-08-22	null	null	null	null	['texture-classification']	['computer-vision']	[ 2.55259216e-01 -2.18469903e-01 -1.07771665e-01 -3.25079829e-01 -3.09122860e-01 -5.46914518e-01 6.32782042e-01 4.39124227e-01 -4.70459521e-01 4.11854953e-01 -1.33946195e-01 5.32606877e-02 -4.89548773e-01 -9.29232180e-01 -5.08288205e-01 -9.06448007e-01 -2.78935194e-01 3.09061408e-01 1.70795575e-01 -3.26092124e-01 2.69877970e-01 9.50898468e-01 -1.90626299e+00 8.02559555e-01 3.35355818e-01 1.61488867e+00 1.81766629e-01 5.53057134e-01 -1.86916143e-01 7.28173494e-01 -5.34388542e-01 -2.17307776e-01 4.23535444e-02 -2.17410326e-02 -8.87102604e-01 9.98092517e-02 5.18957794e-01 -2.45928839e-01 3.23644541e-02 9.39939082e-01 1.14077456e-01 -1.54102176e-01 1.07305038e+00 -9.86006081e-01 -5.93875647e-01 4.44254428e-01 -3.26246917e-01 1.17724314e-01 2.88875531e-02 -1.66875944e-01 9.68512297e-01 -8.07586133e-01 7.30532825e-01 1.10749114e+00 8.09128165e-01 8.18102434e-02 -1.41391468e+00 -2.12569430e-01 -4.28575054e-02 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a6a6b447-ce17-4d41-9be2-ad9216117c75	pareto-policy-adaptation	null	null	https://openreview.net/forum?id=wfZGut6e09	https://openreview.net/pdf?id=wfZGut6e09	Pareto Policy Adaptation	We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for Pareto optimality, we are able to design a simple policy gradient algorithm that approximates the Pareto front and infers the unknown preferences. Our method relies on a projected gradient descent solver that identifies common ascent directions for all objectives. Leveraging the solution of that solver, we introduce Pareto Policy Adaptation (PPA), a loss function that adapts the policy to be optimal with respect to any distribution over preferences. PPA uses implicit differentiation to back-propagate the loss gradient bypassing the operations of the projected gradient descent solver. Our approach is straightforward, easy to implement and can be used with all existing policy gradient and actor-critic methods. We evaluate our method in a series of reinforcement learning tasks	['Paul Bogdan', 'Jyotirmoy Deshmukh', 'Panagiotis Kyriakis']	2021-09-29	null	null	null	iclr-2022-4	['multi-objective-reinforcement-learning']	['methodology']	[-2.70690352e-01 -2.14865757e-03 -3.39171052e-01 -9.71433967e-02 -8.04394484e-01 -8.26616943e-01 3.15322846e-01 3.69925685e-02 -6.10304952e-01 1.41463947e+00 2.09293738e-01 -4.87557560e-01 -3.79027933e-01 -4.71459806e-01 -6.66047752e-01 -6.34400129e-01 1.89599693e-02 7.68403411e-01 -1.48101822e-01 -4.20662940e-01 5.42572916e-01 4.54055816e-01 -1.34758329e+00 1.54333010e-01 1.06199920e+00 8.86987150e-01 1.12071633e-02 8.60425472e-01 -9.38228369e-02 8.57336581e-01 -4.19357091e-01 -2.25153700e-01 3.48762095e-01 -5.68197846e-01 -9.74442124e-01 -7.06527233e-02 1.20510817e-01 -4.26715821e-01 1.91866621e-01 9.23534214e-01 6.19269729e-01 3.57839853e-01 6.26198828e-01 -1.38313854e+00 -1.37347981e-01 3.70417386e-01 -3.71643811e-01 -8.82373564e-03 3.01324129e-01 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a7d7a443-c4bf-4011-b2de-b2932f5a66b2	a-bayesian-approach-to-graph-partitioning	2204.12927	null	https://arxiv.org/abs/2204.12927v1	https://arxiv.org/pdf/2204.12927v1.pdf	A Bayesian Approach To Graph Partitioning	A new algorithm based on bayesian inference for learning local graph conductance based on Gaussian Process(GP) is given that uses advanced MCMC convergence ideas to create a scalable and fast algorithm for convergence to stationary distribution which is provided to learn the bahavior of conductance when traversing the indirected weighted graph. First metric embedding is used to represent the vertices of the graph. Then, uniform induced conductance is calculated for training points. Finally, in the learning step, a gaussian process is used to approximate the uniform induced conductance. MCMC is used to measure uncertainty of estimated hyper-parameters.	['Farshad Noravesh']	2022-04-24	null	null	null	null	['graph-partitioning']	['graphs']	[-7.86733925e-02 8.82051513e-02 -1.89199090e-01 -1.83338553e-01 -8.56384933e-01 -2.21905947e-01 6.27394855e-01 4.14930791e-01 -3.65907520e-01 8.49173725e-01 -8.86740908e-02 -4.38540488e-01 -2.77976871e-01 -1.12542307e+00 -5.53961754e-01 -1.03692245e+00 -4.42890882e-01 6.49447739e-01 3.57822210e-01 5.37803054e-01 6.83471203e-01 2.73836225e-01 -7.40800202e-01 -4.07677919e-01 8.82377088e-01 7.19374299e-01 1.79548189e-01 9.93910551e-01 -3.21899444e-01 4.19403106e-01 -1.59298554e-01 -2.94887125e-01 -5.96768297e-02 -3.48256022e-01 -8.06689382e-01 -2.53513724e-01 -2.10466117e-01 -7.09460005e-02 -2.40024269e-01 1.32824039e+00 2.65339315e-01 4.06431556e-01 1.55354261e+00 -1.05757296e+00 -2.32317343e-01 8.12816560e-01 -8.37218225e-01 7.40061328e-02 2.48801395e-01 -1.51218930e-02 1.18216908e+00 -8.99642348e-01 4.40950304e-01 1.57611239e+00 4.94468212e-01 2.39229664e-01 -1.71881497e+00 -5.12297630e-01 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9ea3741c-f875-4ba3-90af-4479c87d9922	gps-an-optimised-hybrid-mpnn-transformer-for	2212.02229	null	https://arxiv.org/abs/2212.02229v2	https://arxiv.org/pdf/2212.02229v2.pdf	GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction	This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task. Our approach implements several key principles from the prior literature. At its core our GPS++ method is a hybrid MPNN/Transformer model that incorporates 3D atom positions and an auxiliary denoising task. The effectiveness of GPS++ is demonstrated by achieving 0.0719 mean absolute error on the independent test-challenge PCQM4Mv2 split. Thanks to Graphcore IPU acceleration, GPS++ scales to deep architectures (16 layers), training at 3 minutes per epoch, and large ensemble (112 models), completing the final predictions in 1 hour 32 minutes, well under the 4 hour inference budget allocated. Our implementation is publicly available at: https://github.com/graphcore/ogb-lsc-pcqm4mv2.	['Dominique Beaini', 'Ladislav Rampášek', 'Deniz Beker', 'Hatem Helal', 'Adam Sanders', 'Sam Maddrell-Mander', 'Zhiyi Li', 'Kerstin Klaser', 'Josef Dean', 'Dominic Masters']	2022-11-18	null	null	null	null	['molecular-property-prediction']	['miscellaneous']	[ 1.84761122e-01 2.06577152e-01 -1.49291247e-01 -2.44929567e-01 -9.87693787e-01 -3.71148139e-01 3.74048769e-01 4.50214684e-01 -3.72378170e-01 1.25175250e+00 -1.42850950e-01 -7.62204587e-01 -1.09069012e-01 -6.88261032e-01 -1.20819056e+00 -8.11664641e-01 -3.97240460e-01 7.46403813e-01 -3.85392196e-02 -1.04006179e-01 3.49042982e-01 6.29689634e-01 -8.21798861e-01 2.58747190e-01 7.06898212e-01 1.19950163e+00 2.12958623e-02 8.89812291e-01 5.56869030e-01 7.20417202e-01 -1.43049797e-02 -5.44155896e-01 4.06493068e-01 1.11491203e-01 -1.02558970e+00 -7.74851441e-01 8.38135064e-01 2.61950847e-02 -4.61520165e-01 8.57475102e-01 1.03922975e+00 3.30112070e-01 5.93104959e-01 -1.12431192e+00 -3.75450313e-01 4.84530836e-01 -3.47274631e-01 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4da10412-63fa-46e0-984c-286acde2013b	what-matters-for-neural-cross-lingual-named	1909.03598	null	https://arxiv.org/abs/1909.03598v1	https://arxiv.org/pdf/1909.03598v1.pdf	What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis	Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages to low-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further analyze how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embeddings, and investigate how model performance varies across entity lengths. Finally, we conduct a case-study on a non-Latin language, Bengali, which suggests that leveraging knowledge from Wikipedia will be a promising direction to further improve the model performances. Our results can shed light on future research for improving cross-lingual NER.	['Xiaolei Huang', 'Nanyun Peng', 'Jonathan May']	2019-09-09	what-matters-for-neural-cross-lingual-named-1	https://aclanthology.org/D19-1672	https://aclanthology.org/D19-1672.pdf	ijcnlp-2019-11	['cross-lingual-ner']	['natural-language-processing']	[-5.92792451e-01 -1.57376096e-01 -3.64804685e-01 -6.13840878e-01 -1.12402463e+00 -8.40668321e-01 4.71348524e-01 4.74398509e-02 -1.16225135e+00 7.41683125e-01 6.03951633e-01 -5.58060050e-01 4.54634637e-01 -7.95193970e-01 -9.29301739e-01 -4.92248647e-02 -1.16093159e-02 2.73762256e-01 -5.69063202e-02 -2.41613016e-01 -1.81403726e-01 5.24907827e-01 -7.46244371e-01 1.90907091e-01 1.12873793e+00 1.23537809e-01 5.96090816e-02 2.51253694e-01 -3.22936773e-01 5.48837006e-01 -3.56368810e-01 -9.44419205e-01 1.83839530e-01 -2.45296478e-01 -9.77752686e-01 -5.47611892e-01 5.87574303e-01 -8.73401240e-02 -1.26905560e-01 9.86715078e-01 7.21384048e-01 1.56176031e-01 6.29891098e-01 -6.27439737e-01 -1.35538018e+00 9.22655344e-01 -2.81801939e-01 1.39965534e-01 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fad28d7d-925a-4206-9bfd-b6911354bc68	microscopic-fine-grained-instance	2010.02818	null	https://arxiv.org/abs/2010.02818v1	https://arxiv.org/pdf/2010.02818v1.pdf	Microscopic fine-grained instance classification through deep attention	Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. Deep learning based vision systems mostly deal with high number of low-resolution images, whereas subtle detail in biomedical images require higher resolution. To bridge this gap, we propose a simple yet effective deep network that performs two tasks simultaneously in an end-to-end manner. First, it utilises a gated attention module that can focus on multiple key instances at high resolution without extra annotations or region proposals. Second, the global structural features and local instance features are fused for final image level classification. The result is a robust but lightweight end-to-end trainable deep network that yields state-of-the-art results in two separate fine-grained multi-instance biomedical image classification tasks: a benchmark breast cancer histology dataset and our new fungi species mycology dataset. In addition, we demonstrate the interpretability of the proposed model by visualising the concordance of the learned features with clinically relevant features.	['Jens Rittscher', 'Yan Xu', 'Eric I-Chao Chang', 'Tapabrata Chakrabort', 'Mengran Fan']	2020-10-06	null	null	null	null	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[ 4.78458226e-01 6.32316023e-02 1.28997058e-01 -5.65326810e-01 -1.14489830e+00 -4.40105528e-01 7.26753116e-01 5.95773637e-01 -6.86174512e-01 8.81903350e-01 -2.69066811e-01 -1.40810370e-01 -2.47663289e-01 -6.44767642e-01 -7.03980684e-01 -1.17414939e+00 -6.98007829e-03 5.98978877e-01 2.94563740e-01 1.38084814e-01 3.98899257e-01 8.98547947e-01 -1.22971499e+00 8.25133264e-01 3.51170093e-01 1.22887719e+00 4.37927902e-01 1.01604986e+00 1.43832266e-02 7.34638512e-01 -3.35833937e-01 -1.75780326e-01 -7.00037330e-02 1.14846624e-01 -9.97557104e-01 1.27823755e-01 6.11207128e-01 -2.00637564e-01 1.12400353e-01 9.44428742e-01 6.45063579e-01 -3.98820937e-01 9.87899423e-01 -6.84153557e-01 -6.08426750e-01 7.90825412e-02 -8.76268208e-01 6.40585542e-01 -1.16261661e-01 2.58590072e-01 8.44284117e-01 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4b9a3b21-03d4-4664-bf4d-197f12b4e2a7	multi-view-gradient-consistency-for-svbrdf	2202.13017	null	https://arxiv.org/abs/2202.13017v1	https://arxiv.org/pdf/2202.13017v1.pdf	Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes under Natural Illumination	This paper presents a process for estimating the spatially varying surface reflectance of complex scenes observed under natural illumination. In contrast to previous methods, our process is not limited to scenes viewed under controlled lighting conditions but can handle complex indoor and outdoor scenes viewed under arbitrary illumination conditions. An end-to-end process uses a model of the scene's geometry and several images capturing the scene's surfaces from arbitrary viewpoints and under various natural illumination conditions. We develop a differentiable path tracer that leverages least-square conformal mapping for handling multiple disjoint objects appearing in the scene. We follow a two-step optimization process and introduce a multi-view gradient consistency loss which results in up to 30-50% improvement in the image reconstruction loss and can further achieve better disentanglement of the diffuse and specular BRDFs compared to other state-of-the-art. We demonstrate the process in real-world indoor and outdoor scenes from images in the wild and show that we can produce realistic renders consistent with actual images using the estimated reflectance properties. Experiments show that our technique produces realistic results for arbitrary outdoor scenes with complex geometry. The source code is publicly available at: https://gitlab.com/alen.joy/multi-view-gradient-consistency-for-svbrdf-estimation-of-complex-scenes-under-natural-illumination	['Charalambos Poullis', 'Alen Joy']	2022-02-25	null	null	null	null	['svbrdf-estimation']	['computer-vision']	[ 4.88707334e-01 -3.70772183e-01 6.87786996e-01 -4.34799224e-01 -1.03534877e+00 -6.87055528e-01 2.33611122e-01 -5.01679301e-01 3.05769350e-02 5.10813713e-01 1.42677464e-02 -1.50157496e-01 5.65687977e-02 -6.28299415e-01 -7.34201550e-01 -6.98255956e-01 2.92222261e-01 3.03930849e-01 1.21679120e-01 6.25966266e-02 4.36992571e-02 6.14379525e-01 -1.54414463e+00 1.61963478e-01 6.79471314e-01 7.66254127e-01 3.95300806e-01 1.10836220e+00 2.32135311e-01 6.06861770e-01 -2.72435453e-02 -2.27770731e-01 6.63727701e-01 -2.79822677e-01 -5.33070385e-01 6.01676047e-01 1.11794567e+00 -6.83733046e-01 -2.32708771e-02 1.05781996e+00 3.46975356e-01 2.08410680e-01 3.86134326e-01 -7.58374810e-01 -4.16272759e-01 -8.06171834e-01 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74bbbdac-b62e-4d00-95ee-cc529a3d9705	infocse-information-aggregated-contrastive	2210.06432	null	https://arxiv.org/abs/2210.06432v3	https://arxiv.org/pdf/2210.06432v3.pdf	InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings	Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constraint brought by this assumption is weak, and a good sentence representation should also be able to reconstruct the original sentence fragments. Therefore, this paper proposes an information-aggregated contrastive learning framework for learning unsupervised sentence embeddings, termed InfoCSE. InfoCSE forces the representation of [CLS] positions to aggregate denser sentence information by introducing an additional Masked language model task and a well-designed network. We evaluate the proposed InfoCSE on several benchmark datasets w.r.t the semantic text similarity (STS) task. Experimental results show that InfoCSE outperforms SimCSE by an average Spearman correlation of 2.60% on BERT-base, and 1.77% on BERT-large, achieving state-of-the-art results among unsupervised sentence representation learning methods. Our code are available at https://github.com/caskcsg/sentemb/tree/main/InfoCSE.	['Songlin Hu', 'Zhongyuan Wang', 'Jizhong Han', 'Zijia Lin', 'Chaochen Gao', 'Xing Wu']	2022-10-08	null	null	null	null	['sentence-embeddings', 'sentence-embeddings']	['methodology', 'natural-language-processing']	[ 9.87985730e-02 1.92482740e-01 -1.76740453e-01 -6.37635767e-01 -7.42458344e-01 -3.42440993e-01 7.77636051e-01 7.20510483e-01 -7.04440653e-01 4.56644654e-01 8.17677557e-01 -6.30927384e-02 1.30743369e-01 -6.68343306e-01 -6.15042746e-01 -5.30313075e-01 1.25571892e-01 1.48925886e-01 1.83805361e-01 -3.49428862e-01 3.87408972e-01 3.72915752e-02 -1.22758234e+00 7.15846539e-01 7.27596521e-01 7.72104919e-01 5.07065237e-01 7.03152537e-01 -4.99428421e-01 8.09452593e-01 -6.18593752e-01 -7.52893925e-01 -1.05168812e-01 -4.45396066e-01 -8.23637068e-01 -2.14770973e-01 5.51238537e-01 5.99345639e-02 -6.34246886e-01 1.16927123e+00 5.32852113e-01 2.60745406e-01 6.09107614e-01 -1.05454481e+00 -1.10202336e+00 9.13357258e-01 -3.64461780e-01 4.67239022e-01 3.72536778e-01 -2.87921697e-01 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6668c651-f722-46f5-8c99-3aa045180b5e	exploring-fine-grained-audiovisual	2207.10664	null	https://arxiv.org/abs/2207.10664v1	https://arxiv.org/pdf/2207.10664v1.pdf	Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset	We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fine-grained visual categorization on images, the counterparts in audio and video fine-grained categorization are relatively unexplored. To encourage advancements in this space, we have carefully constructed the SSW60 dataset to enable researchers to experiment with classifying the same set of categories in three different modalities: images, audio, and video. The dataset covers 60 species of birds and is comprised of images from existing datasets, and brand new, expert-curated audio and video datasets. We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods. Our findings show that performance of audiovisual fusion methods is better than using exclusively image or audio based methods for the task of video classification. We also present interesting modality transfer experiments, enabled by the unique construction of SSW60 to encompass three different modalities. We hope the SSW60 dataset and accompanying baselines spur research in this fascinating area.	['Serge Belongie', 'Oisin Mac Aodha', 'Hartwig Adam', 'Kimberly Wilber', 'Rui Qian', 'Grant van Horn']	2022-07-21	null	null	null	null	['video-classification', 'fine-grained-visual-categorization']	['computer-vision', 'computer-vision']	[ 3.36563140e-01 -5.91758132e-01 -8.39322731e-02 -2.18344525e-01 -9.81391549e-01 -9.13355947e-01 8.96736860e-01 6.90132007e-02 -4.80081081e-01 3.92058790e-01 5.73258519e-01 -1.07532591e-01 -8.11907873e-02 -3.01878095e-01 -4.44152772e-01 -5.62910557e-01 -2.14580283e-01 -1.18000478e-01 3.43463987e-01 -2.69327372e-01 2.50579476e-01 1.98566377e-01 -2.18777585e+00 8.72324646e-01 7.43838996e-02 1.38691795e+00 -5.33587299e-02 9.76726472e-01 2.25009218e-01 8.11121821e-01 -4.26416337e-01 -3.43953460e-01 2.69270152e-01 -2.91352570e-01 -9.37249660e-01 4.72379439e-02 1.09202445e+00 -1.22026101e-01 -3.14825445e-01 8.26924980e-01 5.73880553e-01 4.04706001e-01 8.61591518e-01 -1.80487394e+00 -5.79863310e-01 4.93675262e-01 -6.40082479e-01 4.94527072e-01 6.79135263e-01 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5e27b29a-c386-4f46-9a99-4b03c0e0f2b2	unexpected-effects-of-online-k-means	1908.06818	null	https://arxiv.org/abs/1908.06818v2	https://arxiv.org/pdf/1908.06818v2.pdf	Unexpected Effects of Online no-Substitution k-means Clustering	Offline k-means clustering was studied extensively, and algorithms with a constant approximation are available. However, online clustering is still uncharted. New factors come into play: the ordering of the dataset and whether the number of points, n, is known in advance or not. Their exact effects are unknown. In this paper we focus on the online setting where the decisions are irreversible: after a point arrives, the algorithm needs to decide whether to take the point as a center or not, and this decision is final. How many centers are needed and sufficient to achieve constant approximation in this setting? We show upper and lower bounds for all the different cases. These bounds are exactly the same up to a constant, thus achieving optimal bounds. For example, for k-means cost with constant k>1 and random order, Theta(log n) centers are enough to achieve a constant approximation, while the mere a priori knowledge of n reduces the number of centers to a constant. These bounds hold for any distance function that obeys a triangle-type inequality.	['Michal Moshkovitz']	2019-08-09	null	null	null	null	['online-clustering']	['computer-vision']	[-3.30247641e-01 -1.89169779e-01 -1.63180158e-01 -1.93451107e-01 -5.16628027e-01 -1.16418386e+00 -1.32878810e-01 9.01574314e-01 -7.25051880e-01 4.05200899e-01 -3.60579431e-01 -4.60462898e-01 -4.58700210e-01 -8.63775313e-01 -8.92447352e-01 -1.20755410e+00 -3.87158483e-01 9.42682505e-01 5.31160831e-01 3.66661549e-02 4.57025051e-01 6.66992605e-01 -1.32963967e+00 -1.58625200e-01 5.96823633e-01 1.01868784e+00 6.59884000e-03 1.10365212e+00 -3.05206686e-01 1.16660468e-01 -4.95788574e-01 -1.64379403e-01 6.13747418e-01 -4.87381160e-01 -9.21313405e-01 2.73998886e-01 -1.07892647e-01 -2.59762585e-01 -1.68878019e-01 1.03079593e+00 2.41638616e-01 1.92390189e-01 5.85203648e-01 -1.48722553e+00 -5.01660407e-01 9.09395039e-01 -8.80851865e-01 1.72503158e-01 6.58606514e-02 -3.75888526e-01 1.00066400e+00 -2.94948429e-01 2.75087327e-01 5.87118804e-01 4.82484281e-01 2.28744835e-01 -1.29303789e+00 -4.15018976e-01 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-4.30240445e-02 1.66738436e-01 -1.60514981e-01 -8.14853728e-01 7.95293152e-02]	[6.7162604331970215, 4.95934534072876]
61a322d8-01f8-42b9-922a-0ee46363f88a	to-adapt-or-to-annotate-challenges-and	2212.10381	null	https://arxiv.org/abs/2212.10381v1	https://arxiv.org/pdf/2212.10381v1.pdf	To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering	Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these models are and how well they perform when applied to real-world applications in drastically different domains. While there has been some work investigating how well ODQA models perform when tested for out-of-domain (OOD) generalization, these studies have been conducted only under conservative shifts in data distribution and typically focus on a single component (ie. retrieval) rather than an end-to-end system. In response, we propose a more realistic and challenging domain shift evaluation setting and, through extensive experiments, study end-to-end model performance. We find that not only do models fail to generalize, but high retrieval scores often still yield poor answer prediction accuracy. We then categorize different types of shifts and propose techniques that, when presented with a new dataset, predict if intervention methods are likely to be successful. Finally, using insights from this analysis, we propose and evaluate several intervention methods which improve end-to-end answer F1 score by up to 24 points.	['Pat Verga', 'Sameer Singh', 'Emma Strubell', 'Dheeru Dua']	2022-12-20	null	null	null	null	['open-domain-question-answering']	['natural-language-processing']	[ 9.69004110e-02 1.21355392e-02 -1.51928857e-01 -5.44019520e-01 -1.43194044e+00 -1.03158677e+00 5.44426858e-01 3.72071743e-01 -4.84800041e-01 7.96660662e-01 4.18278873e-01 -4.62977797e-01 -3.91514510e-01 -6.78970635e-01 -7.42253065e-01 3.20713315e-03 2.44334519e-01 1.06431258e+00 5.63979208e-01 -5.69876134e-01 4.70611513e-01 -1.06792726e-01 -1.63060749e+00 5.48409700e-01 1.36575460e+00 7.96591222e-01 -2.37415791e-01 8.63579154e-01 -2.76408702e-01 1.02193677e+00 -8.99731517e-01 -6.46304727e-01 2.42401168e-01 -3.68513852e-01 -1.47995543e+00 -4.77994472e-01 9.14287031e-01 -4.49729890e-01 -1.44368187e-01 6.68861687e-01 8.57378781e-01 2.84237802e-01 7.07126319e-01 -1.01242816e+00 -9.81704295e-01 2.82137632e-01 -1.38051286e-02 5.90427637e-01 1.01543367e+00 3.75810266e-01 1.24411142e+00 -6.57693744e-01 8.64198208e-01 1.19566417e+00 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2.51338277e-02 -1.13332880e+00 -1.16019964e+00 -6.35142028e-02 8.36749673e-01 2.64661491e-01 -5.42684078e-01 -1.46767199e-01 -2.36995384e-01]	[11.340867042541504, 8.011509895324707]
ea759863-23db-49e1-8831-82fbccc463eb	assessing-cross-cultural-alignment-between	2303.17466	null	https://arxiv.org/abs/2303.17466v2	https://arxiv.org/pdf/2303.17466v2.pdf	Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study	The recent release of ChatGPT has garnered widespread recognition for its exceptional ability to generate human-like responses in dialogue. Given its usage by users from various nations and its training on a vast multilingual corpus that incorporates diverse cultural and societal norms, it is crucial to evaluate its effectiveness in cultural adaptation. In this paper, we investigate the underlying cultural background of ChatGPT by analyzing its responses to questions designed to quantify human cultural differences. Our findings suggest that, when prompted with American context, ChatGPT exhibits a strong alignment with American culture, but it adapts less effectively to other cultural contexts. Furthermore, by using different prompts to probe the model, we show that English prompts reduce the variance in model responses, flattening out cultural differences and biasing them towards American culture. This study provides valuable insights into the cultural implications of ChatGPT and highlights the necessity of greater diversity and cultural awareness in language technologies.	['Daniel Hershcovich', 'Min Chen', 'Laura Cabello', 'Seolhwa Lee', 'Li Zhou', 'Yong Cao']	2023-03-30	null	null	null	null	['culture']	['speech']	[-2.43424729e-01 4.54039797e-02 -4.80632186e-02 -2.90334195e-01 -4.07609522e-01 -8.39620948e-01 7.04025447e-01 7.06394538e-02 -4.83182043e-01 7.49473393e-01 9.59577441e-01 -4.54681486e-01 2.66019344e-01 -3.58251303e-01 -7.63961524e-02 -1.58896789e-01 4.68949258e-01 2.84136474e-01 -1.91559672e-01 -7.99884200e-01 5.27072012e-01 -1.26532555e-01 -9.68722284e-01 4.26151663e-01 1.23275471e+00 -1.45341098e-01 2.83124357e-01 6.76180899e-01 -3.80543143e-01 1.06858623e+00 -9.90790963e-01 -7.47382939e-01 -1.75028577e-01 -8.07782531e-01 -1.02293205e+00 -2.26914868e-01 3.69401723e-01 -4.09550667e-01 1.30717829e-01 7.13444293e-01 6.28938198e-01 1.78619564e-01 4.31769192e-01 -8.22904408e-01 -1.14055824e+00 7.64994144e-01 -2.05175709e-02 6.49425536e-02 9.31913495e-01 1.02167673e-01 7.55525887e-01 -6.07893944e-01 1.10341251e+00 1.39265835e+00 9.20435846e-01 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d7f0f984-73a5-4394-bc88-5b8d80bbe987	neural-preset-for-color-style-transfer	2303.13511	null	https://arxiv.org/abs/2303.13511v2	https://arxiv.org/pdf/2303.13511v2.pdf	Neural Preset for Color Style Transfer	In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed. Our method is based on two core designs. First, we propose Deterministic Neural Color Mapping (DNCM) to consistently operate on each pixel via an image-adaptive color mapping matrix, avoiding artifacts and supporting high-resolution inputs with a small memory footprint. Second, we develop a two-stage pipeline by dividing the task into color normalization and stylization, which allows efficient style switching by extracting color styles as presets and reusing them on normalized input images. Due to the unavailability of pairwise datasets, we describe how to train Neural Preset via a self-supervised strategy. Various advantages of Neural Preset over existing methods are demonstrated through comprehensive evaluations. Notably, Neural Preset enables stable 4K color style transfer in real-time without artifacts. Besides, we show that our trained model can naturally support multiple applications without fine-tuning, including low-light image enhancement, underwater image correction, image dehazing, and image harmonization. Project page with demos: https://zhkkke.github.io/NeuralPreset .	['Rynson W. H. Lau', 'Nanxuan Zhao', 'Lei Zhu', 'Yuhao Liu', 'Zhanghan Ke']	2023-03-23	null	http://openaccess.thecvf.com//content/CVPR2023/html/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.pdf	cvpr-2023-1	['image-dehazing', 'image-enhancement', 'low-light-image-enhancement', 'image-harmonization']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 5.41849196e-01 -4.60603327e-01 3.07840675e-01 -3.18587482e-01 -5.12917280e-01 -6.42410219e-01 1.79831520e-01 -4.42366511e-01 -5.55176735e-01 6.70131624e-01 -6.84947744e-02 -2.56515682e-01 1.31953776e-01 -6.89813256e-01 -9.80318010e-01 -5.74203372e-01 4.87853527e-01 -1.68864056e-01 9.02249739e-02 -4.40905541e-01 3.46793562e-01 4.05472398e-01 -1.52250981e+00 1.75820693e-01 1.28561401e+00 1.11449134e+00 2.85857737e-01 7.48103201e-01 -2.05891445e-01 4.99950349e-01 -6.53438032e-01 -5.31825483e-01 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e99ddfd5-6d88-478e-a040-08924c00eced	thousand-to-one-semantic-prior-modeling-for	2103.07131	null	https://arxiv.org/abs/2103.07131v2	https://arxiv.org/pdf/2103.07131v2.pdf	Thousand to One: Semantic Prior Modeling for Conceptual Coding	Conceptual coding has been an emerging research topic recently, which encodes natural images into disentangled conceptual representations for compression. However, the compression performance of the existing methods is still sub-optimal due to the lack of comprehensive consideration of rate constraint and reconstruction quality. To this end, we propose a novel end-to-end semantic prior modeling-based conceptual coding scheme towards extremely low bitrate image compression, which leverages semantic-wise deep representations as a unified prior for entropy estimation and texture synthesis. Specifically, we employ semantic segmentation maps as structural guidance for extracting deep semantic prior, which provides fine-grained texture distribution modeling for better detail construction and higher flexibility in subsequent high-level vision tasks. Moreover, a cross-channel entropy model is proposed to further exploit the inter-channel correlation of the spatially independent semantic prior, leading to more accurate entropy estimation for rate-constrained training. The proposed scheme achieves an ultra-high 1000x compression ratio, while still enjoying high visual reconstruction quality and versatility towards visual processing and analysis tasks.	['Siwei Ma', 'Jian Zhang', 'Chuanmin Jia', 'Lingbo Yang', 'Zhenghui Zhao', 'Jianhui Chang']	2021-03-12	null	null	null	null	['texture-synthesis']	['computer-vision']	[ 5.87531567e-01 6.23094104e-02 -3.71375322e-01 -3.80213052e-01 -8.08648944e-01 -1.52399406e-01 4.41320240e-01 3.31458412e-02 -4.23650518e-02 6.09565914e-01 6.49450064e-01 1.33490656e-02 -2.83593297e-01 -8.23078036e-01 -5.40989399e-01 -9.12120521e-01 2.64199108e-01 1.72933284e-02 -1.30827427e-01 1.81070536e-01 2.39424616e-01 8.68994221e-02 -1.55419981e+00 2.61788070e-01 8.79779935e-01 1.37332141e+00 6.19073868e-01 6.30594790e-01 1.32726952e-01 7.12250829e-01 2.84638479e-02 -5.56540549e-01 4.09497879e-02 -5.29276550e-01 -5.89693308e-01 2.40494683e-01 -1.95919517e-02 -6.24954820e-01 -8.02061737e-01 1.17000747e+00 2.30791733e-01 -1.09977931e-01 6.23774350e-01 -5.88385642e-01 -5.92803597e-01 2.88581222e-01 -6.98017538e-01 -1.24574527e-01 -1.32453432e-02 2.73335934e-01 1.13212013e+00 -5.17683625e-01 4.52492595e-01 1.13357151e+00 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e1b5e413-1cfe-4902-98c4-b4f0bfc14d0a	combining-stereo-disparity-and-optical-flow	1801.0472	null	http://arxiv.org/abs/1801.04720v1	http://arxiv.org/pdf/1801.04720v1.pdf	Combining Stereo Disparity and Optical Flow for Basic Scene Flow	Scene flow is a description of real world motion in 3D that contains more information than optical flow. Because of its complexity there exists no applicable variant for real-time scene flow estimation in an automotive or commercial vehicle context that is sufficiently robust and accurate. Therefore, many applications estimate the 2D optical flow instead. In this paper, we examine the combination of top-performing state-of-the-art optical flow and stereo disparity algorithms in order to achieve a basic scene flow. On the public KITTI Scene Flow Benchmark we demonstrate the reasonable accuracy of the combination approach and show its speed in computation.	['Oliver Wasenmüller', 'René Schuster', 'Didier Stricker', 'Christian Bailer']	2018-01-15	null	null	null	null	['scene-flow-estimation']	['computer-vision']	[-2.03697145e-01 -7.37656355e-01 -4.35207337e-02 -4.23027277e-01 -2.30331883e-01 -5.29959023e-01 6.49736643e-01 -5.09554386e-01 -3.89810920e-01 9.66163695e-01 -1.18769594e-01 -5.39191425e-01 1.09217905e-01 -5.79301953e-01 -4.13048655e-01 -3.45246047e-01 1.83739841e-01 2.35276207e-01 7.04508424e-01 -3.80858570e-01 6.55676186e-01 8.19945395e-01 -1.76178432e+00 6.44482002e-02 4.86457825e-01 7.99563408e-01 9.31888819e-02 1.07979870e+00 -3.35067987e-01 1.12654591e+00 -3.59529912e-01 -3.93433332e-01 6.75825536e-01 -6.05392218e-01 -8.39039266e-01 2.28404969e-01 1.19696450e+00 -7.19548821e-01 -6.71085954e-01 1.01646495e+00 2.32875094e-01 1.39922246e-01 4.29259956e-01 -1.45328081e+00 2.77981795e-02 -3.81269634e-01 -4.48875934e-01 5.76040566e-01 9.28460300e-01 5.19232333e-01 6.30630374e-01 -8.41046810e-01 1.07253850e+00 1.30479252e+00 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2.80412823e-01 -4.48618740e-01 -5.14076412e-01 7.10274696e-01 7.98203290e-01 -7.79387176e-01 -5.65201819e-01 4.06963110e-01]	[8.706781387329102, -1.7970738410949707]
26003976-4508-453c-a70e-7fc7e6de1d2c	2305-14387	2305.14387	null	https://arxiv.org/abs/2305.14387v1	https://arxiv.org/pdf/2305.14387v1.pdf	AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback	Large language models (LLMs) such as ChatGPT have seen widespread adoption due to their ability to follow user instructions well. Developing these LLMs involves a complex yet poorly understood workflow requiring training with human feedback. Replicating and understanding this instruction-following process faces three major challenges: the high cost of data collection, the lack of trustworthy evaluation, and the absence of reference method implementations. We address these challenges with AlpacaFarm, a simulator that enables research and development for learning from feedback at a low cost. First, we design LLM prompts to simulate human feedback that are 45x cheaper than crowdworkers and display high agreement with humans. Second, we propose an automatic evaluation and validate it against human instructions obtained on real-world interactions. Third, we contribute reference implementations for several methods (PPO, best-of-n, expert iteration, and more) that learn from pairwise feedback. Finally, as an end-to-end validation of AlpacaFarm, we train and evaluate eleven models on 10k pairs of real human feedback and show that rankings of models trained in AlpacaFarm match rankings of models trained on human data. As a demonstration of the research possible in AlpacaFarm, we find that methods that use a reward model can substantially improve over supervised fine-tuning and that our reference PPO implementation leads to a +10% improvement in win-rate against Davinci003. We release all components of AlpacaFarm at https://github.com/tatsu-lab/alpaca_farm.	['Tatsunori B. Hashimoto', 'Percy Liang', 'Carlos Guestrin', 'Jimmy Ba', 'Ishaan Gulrajani', 'Tianyi Zhang', 'Rohan Taori', 'Xuechen Li', 'Yann Dubois']	2023-05-22	null	null	null	null	['instruction-following']	['natural-language-processing']	[-3.50969672e-01 1.45698816e-01 2.16672912e-01 -6.58390284e-01 -1.01731181e+00 -7.05093384e-01 5.93497217e-01 2.24104747e-01 -6.72128081e-01 7.32896626e-01 3.29921275e-01 -5.99804521e-01 1.03976861e-01 -1.38100952e-01 -8.57773364e-01 3.17734741e-02 3.45403031e-02 8.38828802e-01 4.22798753e-01 -4.96228039e-01 3.80711198e-01 -3.18154544e-02 -1.44259286e+00 7.38998652e-01 7.90183067e-01 3.96096319e-01 1.88383490e-01 1.24195087e+00 3.42301905e-01 1.03289366e+00 -8.19196403e-01 -5.22896290e-01 3.52509946e-01 -2.03775942e-01 -1.13790751e+00 -4.41559315e-01 7.18873382e-01 -6.92998648e-01 1.65362597e-01 4.45563465e-01 7.01080322e-01 2.28298992e-01 3.48168582e-01 -1.42800522e+00 -3.61938536e-01 7.74043322e-01 -1.41042605e-01 -4.23108414e-02 7.06733942e-01 5.66563129e-01 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6.58226550e-01 8.62046599e-01 5.45169592e-01 1.29351735e-01 3.88720125e-01 -9.44860816e-01 -1.08181143e+00 -1.36478543e+00 6.26942366e-02 6.94780409e-01 5.63281663e-02 -3.12924683e-01 -3.94897491e-01 -1.75398126e-01]	[12.487785339355469, 8.075278282165527]
6572aa5c-adec-4b60-ad1e-325e448c7b8a	visual-analysis-of-ontology-matching-results	2004.12628	null	https://arxiv.org/abs/2004.12628v1	https://arxiv.org/pdf/2004.12628v1.pdf	Visual Analysis of Ontology Matching Results with the MELT Dashboard	In this demo, we introduce MELT Dashboard, an interactive Web user interface for ontology alignment evaluation which is created with the existing Matching EvaLuation Toolkit (MELT). Compared to existing, static evaluation interfaces in the ontology matching domain, our dashboard allows for interactive self-service analyses such as a drill down into the matcher performance for data type properties or into the performance of matchers within a certain confidence threshold. In addition, the dashboard offers detailed group evaluation capabilities that allow for the application in broad evaluation campaigns such as the Ontology Alignment Evaluation Initiative (OAEI).	['Sven Hertling', 'Jan Portisch', 'Heiko Paulheim']	2020-04-27	null	null	null	null	['ontology-matching']	['knowledge-base']	[-1.21224783e-01 4.64734137e-01 -1.24090470e-01 -6.27986729e-01 -3.59847128e-01 -3.90357524e-01 5.45227170e-01 7.84705162e-01 -1.45793065e-01 1.57584786e-01 6.39737666e-01 -4.43772703e-01 -7.36842930e-01 -1.04506767e+00 -2.94197481e-02 1.93657964e-01 -6.31858930e-02 9.79608774e-01 6.09407365e-01 -6.88765705e-01 1.12689368e-01 2.49783501e-01 -2.18576288e+00 6.28276944e-01 9.28243041e-01 8.27930391e-01 -3.27520192e-01 2.41282701e-01 -4.93573040e-01 5.55229247e-01 -5.28209090e-01 -5.93541205e-01 1.71334356e-01 1.13976978e-01 -1.02956641e+00 -6.52939975e-01 6.46040022e-01 -1.82073653e-01 1.63998127e-01 1.20294905e+00 6.13109350e-01 -8.57677162e-02 -8.88633579e-02 -1.81142509e+00 -2.02895656e-01 8.58468592e-01 3.23763728e-01 1.39607742e-01 1.24781144e+00 -2.56756842e-02 1.17390501e+00 -7.66354144e-01 1.21218634e+00 1.30327582e+00 6.91954017e-01 2.15870127e-01 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43120b82-65ef-4049-a0e6-36a4a01bc083	a-closed-loop-sleep-modulation-system-with	2211.13128	null	https://arxiv.org/abs/2211.13128v1	https://arxiv.org/pdf/2211.13128v1.pdf	A Closed-loop Sleep Modulation System with FPGA-Accelerated Deep Learning	Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device. Sleep stage classification is performed using a lightweight deep learning (DL) model accelerated by a low-power field-programmable gate array (FPGA) device. The DL model uses a single channel electroencephalogram (EEG) as input. Two convolutional neural networks (CNNs) are used to capture general and detailed features, and a bidirectional long-short-term memory (LSTM) network is used to capture time-variant sequence features. An 8-bit quantization is used to reduce the computational cost without compromising performance. The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85.8% and a F1-score of 79%. The developed model has also shown the potential to be generalized to different channels and input data lengths. Closed-loop in-phase auditory stimulation has been demonstrated on the test bench.	['Xilin Liu', 'Yuhan Hou', 'Yaqian Xu', 'Naize Yang', 'Aaron Zhou', 'Mingzhe Sun']	2022-11-19	null	null	null	null	['sleep-quality-prediction']	['medical']	[ 3.15020651e-01 -4.99164045e-01 -2.02208564e-01 -4.45278466e-01 -3.18861067e-01 -9.71038640e-02 -2.23428339e-01 2.67051369e-01 -7.85280585e-01 7.55940020e-01 -3.60338449e-01 -3.35626513e-01 1.44516647e-01 -4.88047719e-01 -1.01708487e-01 -6.20589197e-01 -1.96305946e-01 -1.42655000e-01 1.61190182e-01 -6.84663504e-02 2.44134530e-01 3.76882315e-01 -1.80141771e+00 3.78972083e-01 6.99072361e-01 1.44812143e+00 4.12969708e-01 5.79595983e-01 2.67363667e-01 2.09604815e-01 -1.02042925e+00 2.88541198e-01 1.42802726e-02 -3.00135225e-01 -4.04769808e-01 -4.21236813e-01 -8.20255205e-02 -1.89758480e-01 -1.14239119e-01 8.23485136e-01 9.32068408e-01 -7.52917603e-02 9.14704055e-02 -1.08077610e+00 -1.37094632e-01 7.52905086e-02 -2.74398248e-03 8.69087040e-01 3.56280684e-01 2.71014899e-01 3.22312713e-01 -4.56269473e-01 -2.81782091e-01 5.38024545e-01 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-2.11669177e-01 -1.11829925e+00 -7.82907665e-01 -3.97680849e-01 6.39685929e-01 -6.86004758e-01 -2.11057872e-01 -1.80212185e-02]	[13.533016204833984, 3.5119614601135254]
11a2314e-fe13-4643-8141-a6a17424848f	xcodeeval-a-large-scale-multilingual	2303.03004	null	https://arxiv.org/abs/2303.03004v3	https://arxiv.org/pdf/2303.03004v3.pdf	xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval	AI systems that can create codes as solutions to problems or assist developers in writing codes can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the evaluation of these models has often been performed in a scattered way on only one or two specific tasks, in a few languages, at a partial granularity (e.g., function) level, and in many cases without proper training data. Even more concerning is that in most cases the evaluation of generated codes has been done in terms of mere lexical overlap with a reference code rather than actual execution. We introduce xCodeEval, the largest executable multilingual multitask benchmark to date consisting of 25M document-level coding examples (16.5B tokens) from about 7.5K unique problems covering up to 11 programming languages with execution-level parallelism. It features a total of seven tasks involving code understanding, generation, translation and retrieval. xCodeEval adopts an execution-based evaluation and offers a multilingual code execution engine, ExecEval that supports unit test based execution in all the 11 languages. To address the challenge of balancing the distributions of text-code samples over multiple attributes in validation/test sets, we further propose a novel data splitting and a data selection schema based on the geometric mean and graph-theoretic principle. Experimental results on all the tasks and languages show xCodeEval is a promising yet challenging benchmark as per the current advancements in language models.	['Shafiq Joty', 'Md Rizwan Parvez', 'Weishi Wang', 'Xuan Long Do', 'M Saiful Bari', 'Mohammad Abdullah Matin Khan']	2023-03-06	null	null	null	null	['program-repair', 'program-synthesis', 'program-repair']	['computer-code', 'computer-code', 'reasoning']	[-1.93724018e-02 -2.06426084e-01 -2.90916443e-01 -2.10734919e-01 -1.15825903e+00 -6.36340201e-01 4.82759297e-01 5.28865039e-01 1.01937070e-01 4.88788754e-01 3.97720095e-03 -8.21111977e-01 -3.79294194e-02 -5.36495090e-01 -9.47880208e-01 -1.06615700e-01 -3.09805840e-01 6.34161890e-01 7.86138773e-02 -3.15567434e-01 5.96104264e-01 -1.55139297e-01 -1.55431890e+00 7.85837829e-01 1.27873075e+00 3.69609654e-01 5.57805300e-01 8.43803763e-01 -5.87808311e-01 1.03456628e+00 -8.19958925e-01 -4.92345363e-01 -9.61096212e-03 -1.67514935e-01 -1.16359448e+00 -2.09965289e-01 3.11217993e-01 1.90064177e-01 2.25078672e-01 1.04661119e+00 2.75572747e-01 -3.98425668e-01 4.80210721e-01 -1.36584282e+00 -9.53513980e-01 1.02027512e+00 -6.22179925e-01 -1.04252975e-02 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3f4b3794-bbac-4798-9e8a-5602b4489676	the-importance-of-open-endedness-for-the-sake	2006.03079	null	https://arxiv.org/abs/2006.03079v1	https://arxiv.org/pdf/2006.03079v1.pdf	The Importance of Open-Endedness (for the Sake of Open-Endedness)	A paper in the recent Artificial Life journal special issue on open-ended evolution (OEE) presents a simple evolving computational system that, it is claimed, satisfies all proposed requirements for OEE (Hintze, 2019). Analysis and discussion of the system are used to support the further claims that complexity and diversity are the crucial features of open-endedness, and that we should concentrate on providing proper definitions for those terms rather than engaging in "the quest for open-endedness for the sake of open-endedness" (Hintze, 2019, p. 205). While I wholeheartedly support the pursuit of precise definitions of complexity and diversity in relation to OEE research, I emphatically reject the suggestion that OEE is not a worthy research topic in its own right. In the same issue of the journal, I presented a "high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems" (Taylor, 2019). In the current brief contribution I apply my framework to Hinzte's model to understand its limitations. In so doing, I demonstrate the importance of studying open-endedness for the sake of open-endedness.	['Tim Taylor']	2020-06-04	null	null	null	null	['artificial-life']	['miscellaneous']	[-6.16644956e-02 3.42268646e-01 -1.32133946e-01 -3.56581546e-02 2.35363424e-01 -7.54120767e-01 5.45640349e-01 1.38881817e-01 -3.10388416e-01 6.63225770e-01 2.97015250e-01 -5.54940999e-01 -8.14541876e-01 -3.37690026e-01 -2.41363376e-01 -3.10953438e-01 1.18715316e-01 -1.71955582e-02 -2.99326897e-01 -8.83949816e-01 7.07717419e-01 1.99033216e-01 -1.94448566e+00 -5.43354750e-01 1.16368711e+00 2.23335862e-01 1.30042464e-01 6.28580570e-01 -2.38819756e-02 6.21228337e-01 -5.88410795e-01 -5.60513198e-01 -3.37600037e-02 -9.18492317e-01 -1.01007044e+00 -1.82817683e-01 -2.78219789e-01 2.14256048e-01 -2.21153442e-02 8.23131084e-01 7.19196141e-01 -5.41345105e-02 5.05900979e-01 -1.26170421e+00 -9.60864663e-01 5.02283454e-01 3.05162668e-01 3.91541392e-01 4.70274568e-01 3.67677689e-01 1.06771100e+00 -5.34841955e-01 9.98972535e-01 5.31096041e-01 1.14533114e+00 7.36588478e-01 -7.75964797e-01 -2.48875648e-01 6.50705025e-02 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8a5b151b-2e99-4c6e-8aa7-c8c635344c97	semicontour-a-semi-supervised-learning	1605.04996	null	http://arxiv.org/abs/1605.04996v1	http://arxiv.org/pdf/1605.04996v1.pdf	SemiContour: A Semi-supervised Learning Approach for Contour Detection	Supervised contour detection methods usually require many labeled training images to obtain satisfactory performance. However, a large set of annotated data might be unavailable or extremely labor intensive. In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accuracy with very limited training data (three labeled images). Specifically, we propose a semi-supervised structured ensemble learning approach for contour detection built on structured random forests (SRF). To allow SRF to be applicable to unlabeled data, we present an effective sparse representation approach to capture inherent structure in image patches by finding a compact and discriminative low-dimensional subspace representation in an unsupervised manner, enabling the incorporation of abundant unlabeled patches with their estimated structured labels to help SRF perform better node splitting. We re-examine the role of sparsity and propose a novel and fast sparse coding algorithm to boost the overall learning efficiency. To the best of our knowledge, this is the first attempt to apply SSL for contour detection. Extensive experiments on the BSDS500 segmentation dataset and the NYU Depth dataset demonstrate the superiority of the proposed method.	['Zizhao Zhang', 'Fuyong Xing', 'Xiaoshuang Shi', 'Lin Yang']	2016-05-17	semicontour-a-semi-supervised-learning-1	http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_SemiContour_A_Semi-Supervised_CVPR_2016_paper.html	http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_SemiContour_A_Semi-Supervised_CVPR_2016_paper.pdf	cvpr-2016-6	['contour-detection']	['computer-vision']	[ 7.28976071e-01 2.36414634e-02 -4.63870764e-01 -4.53855693e-01 -9.18519318e-01 -4.43159133e-01 1.45269707e-01 -8.31396133e-02 -1.50293916e-01 6.30588353e-01 1.82877406e-02 -2.13674828e-01 7.21932799e-02 -7.09693789e-01 -5.09976447e-01 -8.93860817e-01 1.82165474e-01 3.49815845e-01 5.06303251e-01 2.91457444e-01 3.47964466e-01 5.93749583e-01 -1.54695547e+00 1.83228374e-01 1.10031569e+00 9.24616992e-01 5.50279021e-01 1.99663490e-01 -3.29949945e-01 8.54011357e-01 -9.97201130e-02 1.46057859e-01 2.80383468e-01 -4.70176101e-01 -7.49970794e-01 9.77025270e-01 5.25727451e-01 -1.77827835e-01 1.76937938e-01 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ab3a62a6-69b1-4a5d-b1f5-176c0f5d5f6e	3d-gan-inversion-for-controllable-portrait	2203.13441	null	https://arxiv.org/abs/2203.13441v1	https://arxiv.org/pdf/2203.13441v1.pdf	3D GAN Inversion for Controllable Portrait Image Animation	Millions of images of human faces are captured every single day; but these photographs portray the likeness of an individual with a fixed pose, expression, and appearance. Portrait image animation enables the post-capture adjustment of these attributes from a single image while maintaining a photorealistic reconstruction of the subject's likeness or identity. Still, current methods for portrait image animation are typically based on 2D warping operations or manipulations of a 2D generative adversarial network (GAN) and lack explicit mechanisms to enforce multi-view consistency. Thus these methods may significantly alter the identity of the subject, especially when the viewpoint relative to the camera is changed. In this work, we leverage newly developed 3D GANs, which allow explicit control over the pose of the image subject with multi-view consistency. We propose a supervision strategy to flexibly manipulate expressions with 3D morphable models, and we show that the proposed method also supports editing appearance attributes, such as age or hairstyle, by interpolating within the latent space of the GAN. The proposed technique for portrait image animation outperforms previous methods in terms of image quality, identity preservation, and pose transfer while also supporting attribute editing.	['Gordon Wetzstein', 'Eric R. Chan', 'David B. Lindell', 'Connor Z. Lin']	2022-03-25	null	null	null	null	['pose-transfer', 'image-animation']	['computer-vision', 'computer-vision']	[ 4.72723752e-01 3.38328809e-01 2.36993865e-03 -4.08951610e-01 -2.04302460e-01 -8.56984854e-01 6.71191037e-01 -4.24934119e-01 -1.06092617e-01 5.47279775e-01 -1.28088519e-01 3.21026564e-01 3.59675795e-01 -8.11053634e-01 -8.88609946e-01 -6.94942653e-01 4.39557046e-01 3.95076245e-01 -2.24789843e-01 -2.19749019e-01 -6.68229833e-02 7.56803811e-01 -1.38706601e+00 -2.27055550e-01 6.49884760e-01 1.02128696e+00 -4.25075084e-01 7.60536969e-01 2.35198632e-01 3.22176635e-01 -7.16141045e-01 -8.13784599e-01 6.46473289e-01 -5.55226266e-01 -2.55126268e-01 5.65110207e-01 1.09278345e+00 -5.30476570e-01 -3.60077620e-01 9.97085869e-01 3.52768630e-01 -1.96505170e-02 5.72957993e-01 -1.56612742e+00 -9.77311909e-01 -9.48983654e-02 -8.14067125e-01 -4.58464205e-01 6.19483352e-01 1.12802334e-01 6.46054506e-01 -4.60131168e-01 9.29575086e-01 1.29654908e+00 6.75087452e-01 9.10315096e-01 -1.36703622e+00 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6.59730315e-01 2.97407955e-01 -6.12010062e-01 -2.40346521e-01 5.26682794e-01]	[12.667744636535645, -0.3598661422729492]
cf56adba-11cf-4e3d-9a3e-edc7480d9479	look-ma-only-400-samples-revisiting-the	2210.02675	null	https://arxiv.org/abs/2210.02675v2	https://arxiv.org/pdf/2210.02675v2.pdf	Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino	With 84.75 million Filipinos online, the ability for models to process online text is crucial for developing Filipino NLP applications. To this end, spelling correction is a crucial preprocessing step for downstream processing. However, the lack of data prevents the use of language models for this task. In this paper, we propose an N-Gram + Damerau Levenshtein distance model with automatic rule extraction. We train the model on 300 samples, and show that despite limited training data, it achieves good performance and outperforms other deep learning approaches in terms of accuracy and edit distance. Moreover, the model (1) requires little compute power, (2) trains in little time, thus allowing for retraining, and (3) is easily interpretable, allowing for direct troubleshooting, highlighting the success of traditional approaches over more complex deep learning models in settings where data is unavailable.	['Dragomir Radev', 'Lorenzo Jaime Yu Flores']	2022-10-06	null	null	null	null	['spelling-correction']	['natural-language-processing']	[-1.33843437e-01 -1.09037690e-01 -3.25679451e-01 -1.38462558e-01 -8.21894169e-01 -1.04766941e+00 7.46159434e-01 6.29670322e-01 -7.67647922e-01 8.49863648e-01 -3.54103558e-03 -1.11897790e+00 -1.35961518e-01 -6.69848204e-01 -6.67124212e-01 -2.64377624e-01 5.09164073e-02 6.59129143e-01 -1.78446025e-01 -2.76689380e-02 2.95553297e-01 8.23263586e-01 -9.18657660e-01 9.68104675e-02 1.26920605e+00 6.51590288e-01 3.73177491e-02 7.23120153e-01 -7.36624449e-02 8.21500719e-01 -5.88149190e-01 -7.91579545e-01 3.48636806e-01 -1.18105307e-01 -7.23482192e-01 -3.98741633e-01 4.69930559e-01 -6.39155567e-01 -4.51681703e-01 9.91334021e-01 3.07014704e-01 2.77081162e-01 7.83766687e-01 -6.56311035e-01 -9.14647281e-01 5.81017494e-01 -9.88989100e-02 4.23042923e-01 6.96155131e-02 1.58131108e-01 1.40125573e+00 -1.05471766e+00 5.96558452e-01 9.43600178e-01 6.88487828e-01 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8d9ae447-3263-442e-be48-e91abe5eb284	accelerating-and-compressing-deep-neural	2304.01914	null	https://arxiv.org/abs/2304.01914v1	https://arxiv.org/pdf/2304.01914v1.pdf	Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback	The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, and downlink channel state information (CSI) compression. However, most of these neural networks are large and inefficient making it a barrier for deployment in practical wireless systems that require low-latency and low memory footprints for individual network functions. To mitigate these limitations, we propose accelerated and compressed efficient neural networks for massive MIMO CSI feedback. Specifically, we have thoroughly investigated the adoption of network pruning, post-training dynamic range quantization, and weight clustering to optimize CSI feedback compression for massive MIMO systems. Furthermore, we have deployed the proposed model compression techniques on commodity hardware and demonstrated that in order to achieve inference gains, specialized libraries that accelerate computations for sparse neural networks are required. Our findings indicate that there is remarkable value in applying these model compression techniques and the proposed joint pruning and quantization approach reduced model size by 86.5% and inference time by 76.2% with minimal impact to model accuracy. These compression methods are crucial to pave the way for practical adoption and deployments of deep learning-based techniques in commercial wireless systems.	['Hatem Abou-zeid', 'Omar Erak']	2023-01-20	null	null	null	null	['model-compression']	['methodology']	[ 2.08936185e-01 -1.89064413e-01 -3.85640204e-01 -4.60670412e-01 -4.36256140e-01 -3.03285997e-02 -3.70803173e-03 1.32425666e-01 -6.06479228e-01 7.65097022e-01 -8.73059686e-03 -8.44606459e-01 -3.06909174e-01 -6.98467016e-01 -8.54679644e-01 -6.30550921e-01 -6.25699341e-01 6.19303137e-02 -8.47640336e-02 -9.14306119e-02 3.54413167e-02 6.27124965e-01 -1.33298910e+00 4.94915359e-02 3.04230183e-01 1.64657223e+00 3.63779306e-01 7.39671350e-01 9.60134938e-02 8.97357523e-01 -6.36637986e-01 -4.66613084e-01 4.60949600e-01 4.69972678e-02 -1.16638593e-01 -5.46531558e-01 2.53691018e-01 -9.82823730e-01 -1.18411851e+00 8.50976884e-01 9.98754919e-01 -1.69415757e-01 3.04029226e-01 -9.77868617e-01 6.64430261e-02 1.10814714e+00 -3.05001289e-01 1.85420528e-01 -2.94006407e-01 -4.31425154e-01 6.63395464e-01 -6.94026828e-01 5.52511066e-02 9.99130249e-01 1.02094817e+00 3.56319547e-01 -6.37148380e-01 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3.65933418e-01 -3.52192849e-01 6.77086785e-02 -2.13424325e-01]	[8.490293502807617, 2.9368550777435303]
76bdd3cd-1ad7-4aa1-8a63-43fb6fe46c6f	entitybert-entity-centric-masking-strategy	null	null	https://aclanthology.org/2021.bionlp-1.21	https://aclanthology.org/2021.bionlp-1.21.pdf	EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain	Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks. However, most models are pretrained on general domain data. We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process. We show that such a model achieves superior results on clinical extraction tasks by comparing our entity-centric masking strategy with classic random masking on three clinical NLP tasks: cross-domain negation detection, document time relation (DocTimeRel) classification, and temporal relation extraction. We also evaluate our models on the PubMedQA dataset to measure the models’ performance on a non-entity-centric task in the biomedical domain. The language addressed in this work is English.	['Guergana Savova', 'Steven Bethard', 'Dmitriy Dligach', 'Timothy Miller', 'Chen Lin']	null	null	null	null	naacl-bionlp-2021-6	['temporal-relation-extraction', 'negation-detection']	['natural-language-processing', 'natural-language-processing']	[ 5.29877007e-01 4.76847649e-01 -4.12866563e-01 -4.38739151e-01 -9.88565743e-01 -3.77273798e-01 5.06491601e-01 6.92203343e-01 -8.78048301e-01 9.26550567e-01 3.65080804e-01 -7.81080663e-01 -1.90158516e-01 -5.56597054e-01 -6.00640595e-01 -3.07326347e-01 -1.81248203e-01 7.33404160e-01 1.95730194e-01 -1.56664833e-01 -2.04257265e-01 5.10763049e-01 -6.50360525e-01 1.04073036e+00 8.89065504e-01 7.56353080e-01 -1.49966225e-01 2.48058975e-01 2.93574519e-02 1.04133356e+00 -6.57661736e-01 -5.57699621e-01 -6.72509000e-02 -3.76889050e-01 -1.05851412e+00 -5.57257354e-01 -1.41235888e-01 2.63760209e-01 -1.42263249e-01 8.37349653e-01 3.98477972e-01 -3.93990457e-01 6.05264068e-01 -9.18450952e-01 -4.29489434e-01 9.28815961e-01 -1.78239807e-01 4.94332850e-01 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