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Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider., , , , , , , , , and 4 other author(s). Nat. Mach. Intell., 4 (2): 154-161 (2022)Symbolic Regression on FPGAs for Fast Machine Learning Inference., , , , , , , , , and . CoRR, (2023)hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices., , , , , , , , , and 20 other author(s). CoRR, (2021)Anomaly Detection with Conditional Variational Autoencoders., , , , and . ICMLA, page 1651-1657. IEEE, (2019)Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider., , , , and . Comput. Softw. Big Sci., (December 2019)Lightweight jet reconstruction and identification as an object detection task., , , , , , , , , and 1 other author(s). Mach. Learn. Sci. Technol., 3 (2): 25016 (2022)Deep learning for inferring cause of data anomalies., , , , , , , , , and 3 other author(s). CoRR, (2017)FIT: A Metric for Model Sensitivity., , , , and . CoRR, (2022)Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider., , , , and . CoRR, (2018)Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring. (Apprentissage pour la détection d'anomalies, avec application au contrôle de qualité d'acquisition de données de l'expérience Compact Muon Solenoid).. University of Paris-Saclay, France, (2020)