{"forum": "SyerXXt8IS", "submission_url": "https://openreview.net/forum?id=SyerXXt8IS", "submission_content": {"title": "Insect Cyborgs: Bio-mimetic Feature Generators Improve ML Accuracy  on Limited Data", "TL;DR": "Features auto-generated by the bio-mimetic MothNet model significantly improve the test accuracy of standard ML methods on vectorized MNIST. The MothNet-generated features also outperform standard feature generators.", "keywords": ["feature selection", "bio-mimesis", "neural networks", "insect olfaction", "sparsity"], "authorids": ["delahunt@uw.edu", "kutz@uw.edu"], "authors": ["Charles B. Delahunt", "J. Nathan Kutz"], "abstract": "We seek to auto-generate stronger  input features  for ML methods faced with limited training data.\nBiological neural nets (BNNs) excel at fast learning, implying that they extract highly informative features. In particular, the insect olfactory network  learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; randomized, sparse connectivity into a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights. In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator. Attached as a front-end pre-processor, MothNet's readout neurons provide new features, derived from the original features, for use by standard ML classifiers. These ``insect cyborgs'' (part BNN and part ML method) have significantly better performance than baseline ML methods alone on vectorized MNIST and Omniglot data sets, reducing test set error averages 20% to 55%. The MothNet feature generator also substantially out-performs other feature generating methods including PCA, PLS, and NNs. These results highlight the potential value of BNN-inspired feature generators in the ML context.", "pdf": "/pdf/e426b57255edb07cb1fed1b33f42bc578d15ce07.pdf", "paperhash": "delahunt|insect_cyborgs_biomimetic_feature_generators_improve_ml_accuracy_on_limited_data"}, "submission_cdate": 1568211741485, "submission_tcdate": 1568211741485, "submission_tmdate": 1571078803539, "submission_ddate": null, "review_id": ["ryxXMYFtvS", "BklfKzC9vS", "H1xtN1RcPH"], "review_url": ["https://openreview.net/forum?id=SyerXXt8IS&noteId=ryxXMYFtvS", "https://openreview.net/forum?id=SyerXXt8IS&noteId=BklfKzC9vS", "https://openreview.net/forum?id=SyerXXt8IS&noteId=H1xtN1RcPH"], "review_cdate": [1569458443515, 1569542777624, 1569541937243], "review_tcdate": [1569458443515, 1569542777624, 1569541937243], "review_tmdate": [1570047552395, 1570047539940, 1570047533621], "review_readers": [["everyone"], ["everyone"], ["everyone"]], "review_writers": [["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper2/AnonReviewer3"], ["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper2/AnonReviewer1"], ["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper2/AnonReviewer2"]], "review_reply_count": [{"replyCount": 0}, {"replyCount": 0}, {"replyCount": 0}], "review_replyto": ["SyerXXt8IS", "SyerXXt8IS", "SyerXXt8IS"], "review_content": [{"title": "feature extraction method based on neural network simulated from olfactory structure", "importance": "3: Important", "importance_comment": "This paper build a biologically inspired neural network from the moth olfactory system and used that as a feature extraction network to preprocess images before using other machine learning algorithms. This is an interesting idea.", "rigor_comment": "The authors identified key component for computation from the moth olfactory system. They showed using this network to preprocess image pixel data and generate features as input for other machine learning algorithm can increase performance.  However, the preprocessing step is like adding more layers to a CNN (more parameters) and this comparison is not convincing enough to show the importance of the biologically inspired network.", "clarity_comment": "The paper is clearly written, but missing some critical control to show the advantage of this feature extraction network.", "clarity": "3: Average readability", "evaluation": "3: Good", "intersection_comment": "Nevertheless, the biologically inspired network is a neat idea and has great potential. ", "intersection": "3: Medium", "comment": "Good as preliminary, but need significant improvement.", "technical_rigor": "2: Marginally convincing", "category": "Neuro->AI"}, {"title": "Lack of details and proper motivation renders MothNet useless", "importance": "1: Irrelevant", "importance_comment": "The relevance of this work was not at all clear. ", "rigor_comment": "Not clear why they did what they did. ", "clarity_comment": "The manuscript is readable but the logic is very unclear. ", "clarity": "3: Average readability", "evaluation": "1: Very poor", "intersection_comment": "Not clear at all what is BNN or how it is relevant. Some claim about faster learning is mentioned. Not sure why. ", "intersection": "1: Very low", "technical_rigor": "2: Marginally convincing", "category": "Not applicable"}, {"title": "An interesting attempt to use neuroscience to inspire AI", "importance": "3: Important", "importance_comment": "The paper presents a biologically-insured model for classification, i.e., Cyborg. \nThe idea of using computational principles in neuroscience to inspire machine learning/AI is an important research direction. I think this paper represents an interesting attempt along this direction. It could help inspiring future endeavors on this topic.", "rigor_comment": "The proposed method attach a previously proposed model MothNet, which is inspired based on the physiology of insect olfaction system to a ML classifier. The MothNet acts as a front-end feature generator. \n\nThe authors compare their method to several baseline methods. They also tried other feature generators rather than MothNet.  Overall, the authors found that Cyborg can achieve better performance on down-sampled, vectorized MNIST, Omnigplot.\n\nThe techniques used are solid in general.", "clarity_comment": "I found the paper is well-written and relatively easy to follow., although the paper would improve if the motivations could be better articulated. ", "clarity": "3: Average readability", "evaluation": "4: Very good", "intersection_comment": "Although the work heavily relies on MothNet, which has been proposed previously. However, the authors' serious effort to combine the ingredients from neuroscience and AI to come out with better method should be applauded. ", "intersection": "3: Medium", "comment": "Overall,  I think this is a quite interesting contribution showing some promise of integrating computational principles learned from neuroscience to ML.  \nA few comments/suggestions.\nFirst, it would be great to see the method tested in more challenging datasets to see if the results generalized. \nSecond, it would be helpful to gain some insights about why the performance improves. One possible idea-  because there are several ingredients in the MothNet, one could keep a subset of these and see how that change the performance.\n", "technical_rigor": "4: Very convincing", "category": "Neuro->AI"}], "comment_id": [], "comment_cdate": [], "comment_tcdate": [], "comment_tmdate": [], "comment_readers": [], "comment_writers": [], "comment_reply_content": [], "comment_content": [], "comment_replyto": [], "comment_url": [], "meta_review_cdate": null, "meta_review_tcdate": null, "meta_review_tmdate": null, "meta_review_ddate ": null, "meta_review_title": null, "meta_review_metareview": null, "meta_review_confidence": null, "meta_review_readers": null, "meta_review_writers": null, "meta_review_reply_count": null, "meta_review_url": null, "decision": "Accept (Poster)"}