Deep Learning Methods In Physics Professors in Global

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Christoph Brune
University of Twente Netherlands
deep learning
geometric deep learning
deep gaussian processes
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Kevin Luck
Vrije Universiteit Amsterdam Netherlands
deep learning
deep reinforcement learning
reinforcement learning
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Yarin Gal
University of Oxford United Kingdom
deep reinforcement learning
deep probabilistic ai
bayesian deep learning
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Yarin Gal
University of Oxford United Kingdom
deep reinforcement learning
deep probabilistic ai
bayesian deep learning
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Molei Tao
Georgia Institute of Technology United States of America
deep learning theory
geometric methods
dynamical systems machine learning
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Jens Walther
Technical University of Denmark Denmark
particle methods
discrete element methods
computational physics
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Kate Hong
Carnegie Mellon University United States of America
computational methods
mathematical methods
physiological methods
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Chaoyue Liu
Purdue University United States of America
deep learning theory
deep learning
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Kin-choong Yow
University of Regina Canada
deep learning
deep reinforcement learning
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Nina Taherimakhsousi
University of Victoria Canada
deep learning
deep reinforcement learning
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Patryk Rygiel
University of Twente Netherlands
deep learning
geometric deep learning
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Anastasios Kratsios
University of Toronto Canada
deep learning
geometric deep learning
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Krishna Swamy
Yale University United States of America
deep learning
geometric deep learning
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Rasmus Paulsen
Technical University of Denmark Denmark
deep reinforcement learning
geometric deep learning
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Jingmin Huang
University of Southampton United Kingdom
deep learning
deep generative models
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Xenia Miscouridou
Imperial College London United Kingdom
deep learning
deep generative models
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Wouter Van Atteveldt
Vrije Universiteit Amsterdam Netherlands
deep learning
computational methods
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Federico Bocci
Radboud University Netherlands
deep learning
computational methods
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Esra Suel
University of Zurich Switzerland
deep learning
computational methods
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Aditi Krishnapriyan
University of California, Berkeley United States of America
geometric deep learning
differentiable physics
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Mikhail Glazunov
Delft University of Technology Netherlands
deep learning
bayesian deep learning
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