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  1. Thorough Characterization and Analysis of Large Transformer Model Training At-Scale

    Article • Proceedings of the ACM on Measurement and Analysis of Computing Systems, February 2024, ACM (Association for Computing Machinery)

    Computational Scientist Sam Foreman, Mahmut Kandemir

  2. Protein Generation via Genome-scale Language Models with Bio-physical Scoring

    Article • November 2023, ACM (Association for Computing Machinery)

    Arvind Ramanathan, Computational Scientist Sam Foreman

  3. HMC with Normalizing Flows

    Article • May 2022, Sissa Medialab, SRL

    Computational Scientist Sam Foreman

  4. LeapfrogLayers: A Trainable Framework for Effective Topological Sampling

    Article • May 2022, Sissa Medialab, SRL

    Computational Scientist Sam Foreman

  5. Machine learning inspired analysis of the Ising model transition

    Article • May 2019, Sissa Medialab, SRL

    Computational Scientist Sam Foreman

  6. Examples of renormalization group transformations for image sets

    Article • November 2018, American Physical Society (APS)

    Computational Scientist Sam Foreman

  7. Large energy density in three-plate nanocapacitors due to Coulomb blockade

    Article • Journal of Applied Physics, March 2018, American Institute of Physics

    Computational Scientist Sam Foreman

  8. RG-inspired machine learning for lattice field theory

    Article • EPJ Web of Conferences, January 2018, EDP Sciences

    Computational Scientist Sam Foreman

  9. Learning better physics

    Article • The University of Iowa

    Computational Scientist Sam Foreman