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Physics of Machine Learning II

ORAL · K09 · ID: 46541

Presentations

  • Finding Spin Glass Ground States Through Deep Reinforcement Learning

    ORAL

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    Presenters

    • Mutian o Shen

      Washington University in St. Louis

    Authors

    • Mutian o Shen

      Washington University in St. Louis

    • Zohar Nussinov

      Washington University in St. Louis

    • Yang-Yu Liu

      Harvard Medical School, Brigham and Women's Hospital (BWH)

    • Changjun Fan

      National University of Defense Technology

    • Yizhou Sun

      University of California, Los Angeles

    • Zhong Liu

      National University of Defense Technology

    View abstract →

  • The random energy landscape of soft-spin networks and its application to combinatorial optimizations

    ORAL

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    Publication: Atsushi Yamamura, Hideo Mabuchi, Surya Ganguli "The random energy landscape of soft-spin networks and its application to combinatorial optimizations", To be published

    Presenters

    • Atsushi Yamamura

      Stanford University

    Authors

    • Atsushi Yamamura

      Stanford University

    • Hideo Mabuchi

      Stanford University

    • Surya Ganguli

      Stanford, Stanford University

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  • Machine learning probing universality class of four models

    ORAL

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    Publication: V. Chertenkov, L. Shchur, Universality classes and machine learning, J. Phys.: Conf. Ser. 1740 (2021) 012003<br>V. Chertenkov, E. Burovski, L. Shchur, On the accuracy of the critical properties estimation of statistical mechanics models using deep learning approach, in preparation

    Presenters

    • Lev Shchur

      Landau ITP - Chernogolovka

    Authors

    • Lev Shchur

      Landau ITP - Chernogolovka

    • Evgeni Burovski

      HSE University, National Research University Higher School of Economics

    • Vladislav Chertenkov

      HSE University

    View abstract →

  • Inverse design of nucleation seeds

    ORAL

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    Presenters

    • Ella M King

      Harvard University

    Authors

    • Ella M King

      Harvard University

    • Chrisy Xiyu Du

      Harvard University

    • Michael P Brenner

      Harvard University

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  • Biased Monte Carlo sampling in RBMs

    ORAL

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    Publication: -Nicolás Bereux, Aurélien Decelle, Cyril Furtlehner, Beatriz Seoane, in preparation.<br>-Aurélien Decelle, Cyril Furtlehner, Beatriz Seoane, accepted for NIPS (2021). Pre-print: ArXiv:2105.13889

    Presenters

    • Beatriz Seoane

      Universidad Complutense de Madrid, Univ Complutense

    Authors

    • Beatriz Seoane

      Universidad Complutense de Madrid, Univ Complutense

    • Aurélien Decelle

      Universidad Complutense de Madrid

    • Cyril Furtlehner

      Paris Saclay University, Inria, Université Paris Saclay

    • Nicolas Bereux

      Paris Saclay University

    View abstract →

  • Nonequilibrium Monte Carlo for unfreezing variables near computational phase transitions

    ORAL

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    Presenters

    • Masoud Mohseni

      Google LLC

    Authors

    • Masoud Mohseni

      Google LLC

    • Daniel K Eppens

      Google LLC

    • Federico Ricci-Tersenghi

      Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy

    • Johan Strumpfer

      Google LLC

    • Alan Ho

      Google LLC

    • Raffaele Marino

      Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy

    • Vasil Denchev

      Google LLC

    • Sergei V Isakov

      Google LLC

    • Sergio Boixo

      Google LLC

    • Hartmut Neven

      Google LLC

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  • An unsupervised neural network learns reproducible and interpretable representations of active matter systems

    ORAL

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    Publication: Liu CJ, Li J, Szurek M, Fakhri N. in prep. Measuring irreversibility in biological active matter.

    Presenters

    • Chih-Wei Joshua Liu

      Massachusetts Institute of Technology

    Authors

    • Chih-Wei Joshua Liu

      Massachusetts Institute of Technology

    • Nikta Fakhri

      Massachusetts Institute of Technology, Department of Physics, Massachusetts Institute of Technology, Massachusetts Institute of Technology MI

    • Junang Li

      Massachusetts Institute of Technology, Department of Physics, Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology MI

    • Michal Szurek

      Massachusetts Institute of Technology MIT

    View abstract →