Statistical Physics Meets Machine Learning I
FOCUS · MAR-F37 · ID: 3102818
Presentations
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Towards a theory of deep learning for hierarchical and compositional data
ORAL · Invited
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Publication: Towards a theory of how the structure of language is acquired by deep neural networks (https://arxiv.org/abs/2406.00048)
Presenters
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Francesco Cagnetta
EPFL, Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Authors
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Francesco Cagnetta
EPFL, Scuola Internazionale Superiore di Studi Avanzati (SISSA)
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Locating Information in Large Language Models via Random Matrix Theory
ORAL
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Publication: https://arxiv.org/html/2410.17770v1
Presenters
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Bernd Rosenow
University Leipzig, University of Leipzig, Leipzig University
Authors
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Bernd Rosenow
University Leipzig, University of Leipzig, Leipzig University
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Max Staats
University of Leipzig
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Matthias Thamm
Leipzig University
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Explaining High-order Interactions in Protein Language Models
ORAL
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Publication: Tsui, Darin, and Amirali Aghazadeh. "On Recovering Higher-order Interactions from Protein Language Models." arXiv preprint arXiv:2405.06645 (2024).
Presenters
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Amirali Aghazadeh
Georgia Institute of Technology
Authors
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Amirali Aghazadeh
Georgia Institute of Technology
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Darin Tsui
Georgia Tech
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Same features, different encodings: three case studies of path dependence in grokking and learning.
ORAL
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Presenters
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Dmitry Manning-Coe
University of Illinois at Urbana-Champaign
Authors
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Dmitry Manning-Coe
University of Illinois at Urbana-Champaign
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Jacopo Gliozzi
University of Illinois at Urbana-Champaign
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Alexander G Stapleton
Queen Mary University of London
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Edward Hirst
Queen Mary University of London
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Marc Klinger
University of Illinois at Urbana-Champaign
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Guiseppe de Tomasi
University of Illinois Urbana-Champaign
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David S Berman
Queen Mary University of London
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The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold
ORAL · Invited
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Publication: 1. Mao, J., Griniasty, I., Teoh, H.K., Ramesh, R., Yang, R., Transtrum, M.K., Sethna, J.P. and Chaudhari, P., 2024. The training process of many deep networks explores the same low-dimensional manifold. Proceedings of the National Academy of Sciences, 121(12), p.e2310002121.<br>2. Ramesh, R., Mao, J., Griniasty, I., Yang, R., Teoh, H.K., Transtrum, M., Sethna, J.P. and Chaudhari, P., 2023. A picture of the space of typical learnable tasks. Proc. of International Conference of Machine Learning (ICML).
Presenters
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Itay Griniasty
Cornell University
Authors
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Itay Griniasty
Cornell University
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Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
ORAL
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Publication: https://arxiv.org/abs/2409.03749
Presenters
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Christian Schmid
University of Oregon
Authors
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Christian Schmid
University of Oregon
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James M Murray
University of Oregon
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Entropy Advantage in Neural Networks Generalizability
ORAL
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Presenters
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Entao Yang
Air Liquide USA, Air Liquide
Authors
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Entao Yang
Air Liquide USA, Air Liquide
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Xiaotian Zhang
City University of Hong Kong
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Yue Shang
University of Pennsylvania
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Ge Zhang
City University of Hong Kong
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Temperature-tuning trained energy functions improves generative performance
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
University of Sydney
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David J Schwab
CUNY Graduate Center, The Graduate Center, CUNY, CUNY
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Stephanie E Palmer
University of Chicago
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Learning continuous spin models with real-valued restricted Boltzmann machines
ORAL
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Publication: https://arxiv.org/abs/2409.20377
Presenters
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Kai Zhang
University of Texas at Tyler
Authors
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Kai Zhang
University of Texas at Tyler
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Abstract Withdrawn
ORAL Withdrawn
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