Data Science and AI/ML in Physics
ORAL · M17 · ID: 2275688
Presentations
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Adaptive machine learning with hard physics constraints for 6D phase space diagnostics of intense charged particle beams
ORAL
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Publication: A. Scheinker, et al. "Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator." Physical Review E 107.4 (2023): 045302.
Presenters
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Alexander Scheinker
Los Alamos Natl Lab
Authors
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Alexander Scheinker
Los Alamos Natl Lab
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New Deep Learning based approach to Primary Vertex finding in ATLAS experiment
ORAL
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Presenters
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rocky B Garg
Stanford University
Authors
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rocky B Garg
Stanford University
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Lauren a Tompkins
Stanford University
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Quantum Machine Learning – Overview, Opportunities and Challenges
ORAL
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Presenters
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J.P. Auffret
George Mason University
Authors
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J.P. Auffret
George Mason University
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Inferring IGM properties from EoR using neural networks
ORAL
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Publication: Inferring IGM parameters from 21-cm power spectrum using Neural Networks (in prep)
Presenters
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Madhurima Choudhury
Brown University
Authors
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Madhurima Choudhury
Brown University
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Abstract Withdrawn
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Abstract Withdrawn
ORAL Withdrawn
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Coherence influx is indispensable for quantum reservoir computing
ORAL
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Presenters
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Shumpei Kobayashi
Department of Creative Informatics, The University of Tokyo
Authors
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Shumpei Kobayashi
Department of Creative Informatics, The University of Tokyo
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Kohei Nakajima
Next Generation Artificial Intelligence Research Center (AI Center), The University of Tokyo, Japan
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Quoc Hoan Tran
Next Generation Artificial Intelligence Research Center (AI Center), The University of Tokyo, Japan
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