Physics of Learning I: Natural systems
FOCUS · A03 · ID: 46150
Presentations
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A mathematical analysis of learning in both biological and artificial neural networks
ORAL · Invited
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Publication: M. Advani and S. Ganguli, Statistical mechanics of optimal convex inference in high dimensions, Physical Review X, 2016.
A.K. Lampinen and S. Ganguli, An analytic theory of generalization dynamics and transfer learning in deep linear networks, International Conference on Learning Representations (ICLR), 2019.
A. Saxe, J. McClelland, S. Ganguli, A. Saxe, J. McClelland, S. Ganguli, Exact solutions to the nonlinear dynamics of learning in deep neural networks, PNAS 2019.
Y. Bahri, J. Kadmon, J. Pennington, S. Schoenholz, J. Sohl-Dickstein, and S. Ganguli, Statistical mechanics of deep learning, Annual Reviews of Condensed Matter Physics, 2020.Presenters
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Surya Ganguli
- Stanford
- Stanford University
Authors
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Surya Ganguli
- Stanford
- Stanford University
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Intrinsic and parameter-less gain control in rate coding by spiking neurons
ORAL
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Presenters
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Nirag Kadakia
- Yale University
Authors
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Nirag Kadakia
- Yale University
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Will Rosenbluth
- Yale University
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Thierry Emonet
- Yale University
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Neural network model of nonlinear Bayesian learning
ORAL
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Presenters
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Camilla Li
- Emory University
Authors
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Camilla Li
- Emory University
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Ilya M Nemenman
- Emory University
- Emory
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Desynchronous and decentralized learning In a physical network
ORAL
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Presenters
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Jacob F Wycoff
- University of Pennsylvania
Authors
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Jacob F Wycoff
- University of Pennsylvania
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Sam J Dillavou
- University of Pennsylvania
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Menachem Stern
- University of Pennsylvania
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Andrea J Liu
- University of Pennsylvania
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Douglas J Durian
- University of Pennsylvania
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Merging Kalman Filtering with the information bottleneck technique for optimal state inference
ORAL
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Presenters
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Vedant Sachdeva
- University of Chicago
Authors
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Vedant Sachdeva
- University of Chicago
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Thierry Mora
- CNRS - Sorbonne University
- Ecole Normale Superieure
- Laboratoire de physique de l'Ecole normale superieure, CNRS
- Laboratoire de physique de l'École normale supérieure
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Arvind Murugan
- University of Chicago
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Stephanie E Palmer
- University of Chicago
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Aleksandra M Walczak
- Ecole Normale Superieure
- CNRS - Sorbonne University
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A theory of weight distribution-constrained learning
ORAL
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Presenters
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Weishun Zhong
- Massachusetts Institute of Technology
Authors
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Weishun Zhong
- Massachusetts Institute of Technology
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Ben Sorscher
- Stanford University
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Daniel D Lee
- Cornell Tech
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Haim I Sompolinsky
- The Hebrew University of Jerusalem and Harvard University
- Hebrew University of Jerusalem
- Center for Brain Science, Harvard Univer
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Biophysical learning : Training flow networks via chemical signaling
ORAL
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Presenters
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Vidyesh Rao Anisetti
- Syracuse University
Authors
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Vidyesh Rao Anisetti
- Syracuse University
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Benjamin Scellier
- ETH Zurich
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Siddhartha Mishra
- ETH Zurich
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Jennifer M Schwarz
- Syracuse University
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How physics-informed neural networks improve image segmentation performance
ORAL
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Presenters
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Daniela Koch
- University of Washington
Authors
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Daniela Koch
- University of Washington
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Exploring the Landscape of Collective Modes Using IB-RG
ORAL
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Presenters
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Adam G Kline
- University of Chicago
Authors
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Adam G Kline
- University of Chicago
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Energy-Based Models Capture Pairwise and Higher-Order Interactions in Protein Sequence Data
ORAL
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Presenters
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Peter Fields
- University of Chicago
Authors
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Peter Fields
- University of Chicago
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Vudtiwat Ngampruetikorn
- The Graduate Center, CUNY
- The Graduate Center, City University of New York
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Stephanie E Palmer
- University of Chicago
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David J Schwab
- The Graduate Center, CUNY
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Learning in gene regulatory networks: dimensionality reduction by master regulators
ORAL
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Publication: H. Schreier, Y. Soen and N. Brenner, "Adaptation by drive reduction in large random networks", Nature Communications 8, 14826 (2017).
A. Rivkind, H. Schreier, N. Brenner and O. Barak, "Scale free topology as an effective feedback system". PLoS Comp. Biol. 16, e1007825 (2020).Presenters
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Naama Brenner
- Technion Israel Institute of Technology
- Technion - Israel Institute of Technolog
Authors
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Naama Brenner
- Technion Israel Institute of Technology
- Technion - Israel Institute of Technolog
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Memory formation in adaptive networks
ORAL
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Publication: Bhattacharyya, Zwicker, Alim PRL (under review)
Presenters
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Komal Bhattacharyya
- MPI for Dynamics and Self-organisation
Authors
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Komal Bhattacharyya
- MPI for Dynamics and Self-organisation
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David Zwicker
- MPI for Dynamics and Self-organisation
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Karen Alim
- TUM
- TU Munich
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The route to chaos of reinforcement learning in routing networks
ORAL
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Publication: Bielawski, J. et. al. Follow-the-Regularized-Leader Routes to Chaos in Routing Games, ICML 2021 (https://arxiv.org/abs/2102.07974)
Chotibut, T. et. al. The route to chaos in routing games: When is price of anarchy too optimistic?, NeurIPS 2020 (https://arxiv.org/abs/1906.02486)Presenters
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Thiparat Chotibut
- Chula Intelligent and Complex Systems Lab, Department of Physics, Chulalongkorn University, Thailand
- Chula Intelligent and Complex Systems Lab, Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Chula Intelligent and Complex Systems Lab, Department of Physics, Faculty of Science, Chulalongkorn University, Thailand
Authors
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Jakub Bielawski
- Department of Mathematics, Cracow University of Economics, Poland
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Thiparat Chotibut
- Chula Intelligent and Complex Systems Lab, Department of Physics, Chulalongkorn University, Thailand
- Chula Intelligent and Complex Systems Lab, Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Chula Intelligent and Complex Systems Lab, Department of Physics, Faculty of Science, Chulalongkorn University, Thailand
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Fryderyk Falniowski
- Department of Mathematics, Cracow University of Economics, Poland
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Michal Misiurewicz
- Department of Mathematical Sciences, Indiana University-Purdue University Indianapolis, USA
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Georgios Piliouras
- Engineering Systems and Design, Singapore University of Technology and Design, Singapore
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