Machine Learning for Quantum Matter II

FOCUS · S47 · ID: 46903

Presentations

  • ORAL · Invited

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    Publication: [1] A. Dawid et al. (2020). Phase detection with neural networks: interpreting the black box. New J. Phys. 22, 115001.
    [2] N. Käming, A. Dawid, K. Kottmann, et al. (2021). Unsupervised machine learning of topological phase transitions from experimental data. Mach. Learn.: Sci. Technol. 2, 035037.
    [3] A. Dawid et al. (2021). Hessian-based toolbox for interpretable and reliable machine learning in physics. Mach. Learn.: Sci. Technol. in press https://doi.org/10.1088/2632-2153/ac338d.

    Presenters

    • Anna Dawid

      • University of Warsaw & ICFO - The Institute of Photonic Sciences

    Authors

    • Anna Dawid

      • University of Warsaw & ICFO - The Institute of Photonic Sciences

    • Patrick Huembeli

      • École Polytechnique Fédérale de Lausanne

    • Michał Tomza

      • University of Warsaw

    • Maciej Lewenstein

      • ICFO - The Institute of Photonic Sciences & ICREA
      • ICFO / ICREA

    • Alexandre Dauphin

      • ICFO - The Institute of Photonic Sciences

    View abstract →

  • ORAL

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    Publication: https://arxiv.org/abs/2109.07356

    Presenters

    • Tom Vieijra

      • Ghent University

    Authors

    • Tom Vieijra

      • Ghent University

    • Jutho Haegeman

      • Ghent University

    • Frank Verstraete

      • Ghent University

    • Laurens Vanderstraeten

      • Ghent University

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  • ORAL

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    Publication: F. Vicentini, R. Rossi, G. Carleo, under preparation (2022)

    Presenters

    • Filippo Vicentini

      • Ecole Polytechnique Federale de Lausanne

    Authors

    • Filippo Vicentini

      • Ecole Polytechnique Federale de Lausanne

    • Riccardo Rossi

      • Ecole Polytechnique Federale de Lausanne

    • Giuseppe Carleo

      • Ecole Polytechnique Federale de Lausanne

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  • ORAL

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    Publication: arXiv:2101.07243

    Presenters

    • Zhuo Chen

      • Massachusetts Institute of Technology

    Authors

    • Zhuo Chen

      • Massachusetts Institute of Technology

    • Di Luo

      • Massachusetts Institute of Technology
      • University of Illinois at Urbana-Champaign

    • Kaiwen Hu

      • University of Michigan—Ann Arbor

    • Zhizhen Zhao

      • University of Illinois at Urbana-Champaign

    • Vera M Hur

      • University of Illinois at Urbana-Champaign

    • Bryan K Clark

      • University of Illinois at Urbana-Champaign

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  • ORAL

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    Presenters

    • Zhantao Chen

      • Massachusetts Institute of Technology MI
      • Massachusetts Institute of Technology

    Authors

    • Zhantao Chen

      • Massachusetts Institute of Technology MI
      • Massachusetts Institute of Technology

    • Nina Andrejevic

      • Massachusetts Institute of Technology MI

    • Tongtong Liu

      • Massachusetts Institute of Technology MI

    • Xiaozhe Shen

      • SLAC National Accelerator Laboratory
      • SLAC
      • SLAC Natl Accelerator Lab

    • Thanh Nguyen

      • Massachusetts Institute of Technology MI

    • Nathan C Drucker

      • Harvard University

    • Mingda Li

      • Massachusetts Institute of Technology
      • Massachusetts Institute of Technology MI

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  • ORAL

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    Presenters

    • Timur Bazhirov

      • Exabyte Inc.

    Authors

    • Timur Bazhirov

      • Exabyte Inc.

    • James Dean

      • Exabyte Inc.

    • Rahul Bhowmik

      • Polaron Analytics

    • Sergey Barabash

      • Intermolecular, Inc.

    • Matthias Scheffler

      • NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
      • Fritz-Haber Institute
      • The NOMAD Laboratory at the Fritz Haber Institute of the MPG

    • Thomas A Purcell

      • Fritz-Haber-Institute
      • Fritz-Haber Institute
      • The NOMAD Laboratory at the Fritz Haber Institute of the MPG

    View abstract →