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Physics of Machine Learning I

FOCUS · F09 · ID: 46543

Presentations

  • Toward Statistical Mechanics of Deep Learning

    ORAL · Invited

    –

    Publication: Qianyi Li, and Haim Sompolinsky (2021). Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Kernel Renormalization. (Physical Review X 11.3 031059) .

    Presenters

    • Haim I Sompolinsky

      The Hebrew University of Jerusalem and Harvard University, Hebrew University of Jerusalem, Center for Brain Science, Harvard Univer

    Authors

    • Haim I Sompolinsky

      The Hebrew University of Jerusalem and Harvard University, Hebrew University of Jerusalem, Center for Brain Science, Harvard Univer

    • Qianyi Li

      Biophysics Program, Harvard University

    View abstract →

  • AI Pontryagin or: How Artificial Neural Networks Learn to Control Dynamical Systems

    ORAL

    –

    Publication: This work is currently under review in Nature Communications. A second, related work is under review in Physical Review Research.

    Presenters

    • Lucas Boettcher

      Frankfurt School of Finance and Management; UCLA, Frankfurt School of Finance & Management gGmbH

    Authors

    • Lucas Boettcher

      Frankfurt School of Finance and Management; UCLA, Frankfurt School of Finance & Management gGmbH

    • Thomas Asikis

      ETH Zurich

    • Nino Antulov-Fantulin

      ETH Zurich

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  • Probing the Theoretical and Computational Limits of Dissipative Design

    ORAL

    –

    Publication: Shriram Chennakesavalu and Grant M. Rotskoff. Probing the theoretical and computational limits of dissipative design, 2021. arXiv: 2108.05452 [cond-mat.stat-mech].

    Presenters

    • Shriram Chennakesavalu

      Stanford University

    Authors

    • Shriram Chennakesavalu

      Stanford University

    • Grant M Rotskoff

      Stanford Univ

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  • Statistical Mechanics of Kernel Regression and Wide Neural Networks

    ORAL

    –

    Publication: Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan, Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks, ICML, 2020<br><br>Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan, Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks, Nature Communications, 2021<br><br>Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan, Out-of-Distribution Generalization in Kernel Regression, NeurIPS, 2021

    Presenters

    • Abdulkadir Canatar

      Harvard University

    Authors

    • Abdulkadir Canatar

      Harvard University

    • Blake Bordelon

      Harvard University

    • Cengiz Pehlevan

      Harvard University

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  • Machine learning in and out of equilibrium

    ORAL

    –

    Presenters

    • Michael Hinczewski

      Case Western Reserve University

    Authors

    • Michael Hinczewski

      Case Western Reserve University

    • Shishir Adhikari

      Harvard Medical School

    • Alkan Kabakcioglu

      Koc University, Koç University

    • Alexander Strang

      University of Chicago

    • Deniz Yuret

      Koç University

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  • Can Artificial Intelligence "formulate" Quantum Mechanics? An Illustration for Planck's Blackbody Radiation

    ORAL

    –

    Publication: In preparation:<br>V. Shankar, S. Shankar, "Can Artificial Intelligence "formulate" Quantum Mechanics? An Illustration for Planck's Blackbody Radiation"

    Presenters

    • Vishnu Shankar

      Stanford University

    Authors

    • Vishnu Shankar

      Stanford University

    • Sadasivan Shankar

      SLAC National Laboratory and Stanford University, Harvard University

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  • Non-Gaussian effects in finite Bayesian neural networks

    ORAL

    –

    Publication: Preprints: https://arxiv.org/abs/2104.11734, https://arxiv.org/abs/2106.00651

    Presenters

    • Jacob Zavatone-Veth

      Harvard University

    Authors

    • Jacob Zavatone-Veth

      Harvard University

    • Abdulkadir Canatar

      Harvard University

    • Benjamin S Ruben

      Harvard University

    • Cengiz Pehlevan

      Harvard University

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  • Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines

    ORAL

    –

    Publication: https://arxiv.org/pdf/2105.13889.pdf accepted in Neurips2021

    Presenters

    • Aurélien Decelle

      Universidad Complutense de Madrid

    Authors

    • Aurélien Decelle

      Universidad Complutense de Madrid

    • Beatriz Seoane

      Universidad Complutense de Madrid, Univ Complutense

    • Cyril Furtlehner

      Paris Saclay University, Inria, Université Paris Saclay

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  • Exploring the loss landscape with Langevin dynamics

    ORAL

    –

    Presenters

    • Théo Jules

      Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University

    Authors

    • Théo Jules

      Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University

    • Yohai Bar-Sinai

      Google LLC, Tel Aviv University

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  • Learning actions from data using invertible neural networks

    ORAL

    –

    Presenters

    • Claudia Merger

      RWTH Aachen University

    Authors

    • Claudia Merger

      RWTH Aachen University

    • Carsten Honerkamp

      RWTH Aachen University

    • Alexandre René

      RWTH Aachen University and University of Ottawa

    • Moritz Helias

      Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre and RWTH Aachen University

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