Model-based Statistical Physics, Computable Data, and Model-Free Artificial Intelligence

INVITED · W56 · ID: 1850305

Presentations

  • On the use of physics in machine learning for imaging and quantifying complex processes

    ORAL · Invited

    –

    Publication: Qihang Zhang, et al, Nature Comm. 14:1159, 2023

    Presenters

    • George Barbastathis

      MIT

    Authors

    • George Barbastathis

      MIT

    • Qihang Zhang

      Singapore-MIT Alliance for Research and Technology Centre; present address: Tsinghua University

    • Richard D Braatz

      Massachusetts Institute of Technology MIT

    • Allan Myerson

      Massachusetts Institute of Technology

    • Charles Papageorgiou

      Takeda Pharmaceuticals

    • Wenlong Tang

      Takeda Pharmaceuticals

    • Yi Wei

      Massachusetts Institute of Technology

    • Neda Nazemifard

      Takeda Pharmaceuticals

    • Deborah Pereg

      Massachusetts Institute of Technology

    • Ajinkya Pandit

      Massachusetts Institute of Technology

    • Shashank Muddu

      Massachusetts Institute of Technology

    • Sandip Mondal

      Sinagpore-MIT Alliance for Research and Technology Centre

    • Daniel Roxby

      Singapore-MIT Alliance for Research and Technology Centre

    • Jongyoon Han

      Massachusetts Institute of Technology MIT

    View abstract →

  • Energy Frontier Exploration using Particle Physics and AI

    ORAL · Invited

    –

    Publication: A. Khot, M.S. Neubauer, A. Roy. (2023). A detailed study of interpretability of deep neural network based top taggers. Mach. Learn. Sci. Tech., 4(3), 035003. https://doi.org/10.1088/2632-2153/ace0a1

    A. Deiana, et al. (2022). Applications and Techniques for Fast Machine Learning in Science. Front. Big Data, 5, 787421. https://doi.org/10.3389/fdata.2022.787421

    Presenters

    • Mark S Neubauer

      University of Illinois at Urbana-Champaign

    Authors

    • Mark S Neubauer

      University of Illinois at Urbana-Champaign

    View abstract →

  • Data-driven medical image formation without a priori models

    ORAL · Invited

    –

    Publication: Hoerig C, Ghaboussi J, Insana MF, "Data-driven elasticity imaging using Cartesian neural network constitutive models and the autoprogressive method," IEEE Trans Med Imaging 38(5):1150-1160, 2019. doi: 10.1109/TMI.2018.2879495. PMID: 30403625.

    Hoerig, C, Ghaboussi J, Wang L, Insana MF, "Machine learning in model-free mechanical property imaging: novel integration of physics with the constrained optimization process," (invited review), Frontiers in Phys, 9:600718. 2021. doi: 10.3389/fphy.2021.600718.

    Presenters

    • Michael Insana

    Authors

    • Michael Insana

    • Will Newman

      University of Illinois at Urbana-Champaign

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  • The Restricted Boltzmann Machine: from the statistical physics of disordered systems to a practical and interpretative generative machine learning.

    ORAL · Invited

    –

    Publication: * Restricted Boltzmann machine: Recent advances and mean-field theory, Chinese Physics B 30 040202 (2021) DOI 10.1088/1674-1056/abd160
    * Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines
    , Neurips conference 2021,
    * Inferring effective couplings with Restricted Boltzmann Machines, arXiv:2309.02292 https://doi.org/10.48550/arXiv.2309.02292
    * Unsupervised hierarchical clustering using the learning dynamics of restricted Boltzmann machines, Phys. Rev. E 108, 014110 (2023) https://doi.org/10.1103/PhysRevE.108.014110

    Presenters

    • Aurélien Decelle

      Universidad Complutense de Madrid

    Authors

    • Aurélien Decelle

      Universidad Complutense de Madrid

    • Beatriz Seoane

      Univ Complutense

    • Lorenzo Rosset

      Laboratoire de physique de l'Ecole normale supérieure (LPENS)

    • Cyril Furtlehner

      INRIA Paris Saclay

    • Nicolas Bereux

      LISN, Université Paris Saclay

    • Giovanni Catania

      Theoretical Physics department, Universidad Complutense de Madrid

    • Elisabeth Agoritsas

      University of Geneva

    View abstract →