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Fast Machine Learning for Science Workshop 2022

Oct 3 – 6, 2022
Southern Methodist University
America/Chicago timezone
  • Overview
  • Call for Abstracts
    • Timetable
    • Contribution List
    • Registration
    • Book of Abstracts
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    • Code of Conduct
    • Cultural/Tourist Activities in Dallas

    Details for Philip Coleman Harris

    Massachusetts Inst. of Technology (US)

    Author in the following contributions

    • A Machine Learning Software Infrastructure for Gravitational Wave Signal Discovery
    • Fast recurrent neural networks on FPGAs with hls4ml
    • Next Generation Coprocessors as a service
    • Rapid Fitting of Band-Excitation Piezoresponse Force Microscopy Using Physics Constrained Unsupervised Neural Networks
    • Low-latency Calorimetry Clustering at the LHC with SPVCNN
    • Demonstration of Machine Learning-assisted real-time noise regression in LIGO
    • End-to-end acceleration of machine learning in gravitational wave physics
    • Increasing the LHC Computational Power by integrating GPUs as a service
    • Design and first test results of a reconfigurable autoencoder on an ASIC for data compression at the HL-LHC
    • Large CNN for HLS4ML and Deepcalo
    • A novel ML-based method of primary vertex reconstruction in high pile-up condition
    CERN
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