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Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC.

, , , , , , , и . Comput. Softw. Big Sci., (декабря 2019)

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Nguyên Huu Thông

Van Thong Nguyen

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Chí-Thông Nguyên

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Printer identification from micro-metric scale printing., , , и . ICASSP, стр. 6236-6239. IEEE, (2014)Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider., , , , , , , , , и 4 other автор(ы). Nat. Mach. Intell., 4 (2): 154-161 (2022)Analysis-Specific Fast Simulation at the LHC with Deep Learning., , , , и . Comput. Softw. Big Sci., (2021)Variational Autoencoders for New Physics Mining at the Large Hadron Collider., , , , и . CoRR, (2018)Topology classification with deep learning to improve real-time event selection at the LHC., , , , , , , и . CoRR, (2018)Natural Language Commanding via Program Synthesis., , , , и . CoRR, (2023)Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning., , , , и . CoRR, (2020)Printer technology authentication from micrometric scan of a single printed dot., , , и . Media Watermarking, Security, and Forensics, том 9028 из SPIE Proceedings, стр. 90280U. SPIE, (2014)Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC., , , , , , , и . Comput. Softw. Big Sci., (декабря 2019)Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider., , , , , , , , , и 4 other автор(ы). Nat. Mach. Intell., 4 (4): 414 (2022)