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ML4Jets2024

4–8 Nov 2024
LPNHE, Paris, France
Europe/Paris timezone
  • Overview
  • Call for Abstracts
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    • Timetable (full details)
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    Details for Ben Nachman

    Lawrence Berkeley National Lab. (US)

    Author in the following contributions

    • The Fundamental Limit of Jet Tagging
    • The Landscape of Unfolding with Machine Learning
    • WOTAN: Weakly-supervised Optimal Transport Attention-based Noise Mitigation
    • Bridging the Generative Unfolding Gap
    • Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
    • OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks
    • Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Diffusion Models
    • Fair Universe HiggsML Uncertainty Challenge
    • The Fast Calorimeter Challenge 2022: Final Evaluation & Lessons Learned
    • Data-driven hadronization models
    • The versatility of flow-based fast calorimeter surrogate models
    • Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
    CERN
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