Datasets:

mfromm
/

AMSR

{"forum": "S1g_N7FIUS", "submission_url": "https://openreview.net/forum?id=S1g_N7FIUS", "submission_content": {"pdf": "/pdf/e014d06826874b295a1c30bd9b20630118d16fad.pdf", "title": "Neocortical plasticity: an unsupervised cake but no free lunch", "abstract": "The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, important inspiration for the development of artificial intelligence systems has come from the study of natural systems of intelligence, the mammalian neocortex in particular. On the other, important inspiration for models and theories of the brain have emerged from artificial intelligence research. A central question at the intersection of these two areas is concerned with the processes by which neocortex learns, and the extent to which they are analogous to the back-propagation training algorithm of deep networks. Matching the data efficiency, transfer and generalisation properties of neocortical learning remains an area of active research in the field of deep learning. Recent advances in our understanding of neuronal, synaptic and dendritic physiology of the neocortex suggest new approaches for unsupervised representation learning, perhaps through a new class of objective functions, which could act alongside or in lieu of back-propagation. Such local learning rules have implicit rather than explicit objectives with respect to the training data, facilitating domain adaptation and generalisation. Incorporating them into deep networks for representation learning could better leverage unlabelled datasets to offer significant improvements in data efficiency of downstream supervised readout learning, and reduce susceptibility to adversarial perturbations, at the cost of a more restricted domain of applicability.\n", "keywords": ["neocortex", "local learning", "dendrites", "adversarial examples", "generalisation"], "authors": ["Eilif B. Muller", "Philippe Beaudoin"], "authorids": ["eilif.muller@elementai.com", "phil@elementai.com"], "TL;DR": "Inspiration from local dendritic processes of neocortical learning to make unsupervised learning great again.", "paperhash": "muller|neocortical_plasticity_an_unsupervised_cake_but_no_free_lunch"}, "submission_cdate": 1568211760058, "submission_tcdate": 1568211760058, "submission_tmdate": 1572552117892, "submission_ddate": null, "review_id": ["S1xqlJ6CLH", "Sye5qRrMvr", "HJe3h_IGwr"], "review_url": ["https://openreview.net/forum?id=S1g_N7FIUS&noteId=S1xqlJ6CLH", "https://openreview.net/forum?id=S1g_N7FIUS&noteId=Sye5qRrMvr", "https://openreview.net/forum?id=S1g_N7FIUS&noteId=HJe3h_IGwr"], "review_cdate": [1568751346464, 1568984722471, 1568987315979], "review_tcdate": [1568751346464, 1568984722471, 1568987315979], "review_tmdate": [1570047569108, 1570047568462, 1570047567365], "review_readers": [["everyone"], ["everyone"], ["everyone"]], "review_writers": [["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper47/AnonReviewer1"], ["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper47/AnonReviewer3"], ["NeurIPS.cc/2019/Workshop/Neuro_AI/Paper47/AnonReviewer2"]], "review_reply_count": [{"replyCount": 0}, {"replyCount": 0}, {"replyCount": 0}], "review_replyto": ["S1g_N7FIUS", "S1g_N7FIUS", "S1g_N7FIUS"], "review_content": [{"title": "Discussion of various ideas in neuroscience and machine learning, but details largely absent", "category": "Neuro->AI", "intersection_comment": "The submission discusses both fields. However, as mentioned, the details in connecting these areas are largely absent.", "evaluation": "2: Poor", "importance_comment": "The submission discusses a few observations about the neocortex and emphasizes the importance of unsupervised learning of representations. However, the connections between these points are unclear, and it is therefore difficult to determine if any novel ideas are proposed. Because the submission does not elaborate on how neocortical principles could assist in improving unsupervised representation learning, the importance of this work seems lacking.", "rigor_comment": "The submission does not present any empirical results or theoretical formulations. Technical concepts are not explored in detail.", "importance": "2: Marginally important", "clarity_comment": "The submission was, at times, difficult to follow. For instance, connections between the neuroscience discussion (e.g. cliques of pyramidal neurons) and better forms of representation learning are unclear.", "intersection": "4: High", "technical_rigor": "2: Marginally convincing", "comment": "I would challenge the authors to expand the discussion around Martinotti neurons to include a model of how such mechanisms could facilitate unsupervised / representation learning.\n\nThe figure could be improved by converting the hand-drawn diagrams into more professional looking graphics.\n\nThe introduction contains several overstated claims. For instance, it\u2019s difficult to say whether the \u201ctask\u201d of synaptic plasticity in the neocortex is to learn disentangled representations. ", "clarity": "2: Can get the general idea"}, {"evaluation": "3: Good", "intersection": "5: Outstanding", "importance_comment": "The importance of understanding unsupervised learning in the brain cannot be understated. If we could emulate the unsupervised learning used by the brain in ANNs it would be a massive leap forward in AI. Thus, the goals of this submission are very important. However, this submission only gestures at potential solutions, so the importance of this specific contribution is more limited.", "clarity": "5: Impeccable", "technical_rigor": "1: Not convincing", "intersection_comment": "It is a perfect mix of neuroscience and AI.", "rigor_comment": "This submission contains much speculation, and some discussion of known biological facts. But, there is no analytical or empirical demonstration that the biological mechanisms described actually would provide the sort of unsupervised learning proposed. Furthermore, the claim that such unsupervised mechanisms would prevent susceptibility to adversarial attacks is unconvincing, and not backed up by any data or math.", "comment": "There are some great ideas in here, and potentially excellent topics for discussion. But, there is very little in terms of actual material contributions. It is essentially an opinion piece.", "importance": "4: Very important", "title": "An interesting proposal, but still very preliminary/loose", "category": "Neuro->AI", "clarity_comment": "It's a very well written submission."}, {"evaluation": "2: Poor", "intersection": "5: Outstanding", "importance_comment": "The importance of the topic covered in this paper - namely, the role of unsupervised learning in biology and artificial intelligence - is very high. However, this point has been highlighted frequently in the past, and this paper stops short of offering any concrete or novel contributions.", "clarity": "4: Well-written", "technical_rigor": "1: Not convincing", "intersection_comment": "This topic surely should and will be discussed at this workshop.", "rigor_comment": "No concrete results or proposals are offered to solve the (important) problem of unsupervised learning in AI and the brain. Experimental findings regarding NMDA-mediated plasticity in cortex are briefly reviewed, but not connected back to this problem, providing little insight into how to solve it.", "comment": "I strongly believe the topics covered in this paper should be discussed in this workshop. However, I do not believe this paper stands to contribute much to such a discussion, as it provides few novel insights or directions. One somewhat novel idea that this reviewer was able to walk away with was the suggestion that, in order to obtain good models of biological learning, we should focus on solving ecologically relevant statistical problems in AI.", "importance": "3: Important", "title": "A critical topic for discussion at the intersection of neuroscience and AI, but few novel ideas proposed", "category": "Common question to both AI & Neuro", "clarity_comment": "Generally well-written."}], "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)"}