Deep Gaussian Approximation Professors in Global

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Christoph Brune
University of Twente Netherlands
deep gaussian processes
geometric deep learning
deep learning
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Yarin Gal
University of Oxford United Kingdom
deep probabilistic ai
deep reinforcement learning
bayesian deep learning
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Mingyuan Zhou
University of Texas at Austin United States of America
deep neural networks
approximate inference
bayesian analysis
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Houman Owhadi
California Institute of Technology United States of America
gaussian process regression
statistical numerical approximation
multi-scale stochastic analysis
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Houman Owhadi
California Institute of Technology United States of America
gaussian process regression
statistical numerical approximation
multi-scale stochastic analysis
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Georgios Arvanitidis
Technical University of Denmark Denmark
deep learning
approximate bayesian inference
differential geometry
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Mikhail Glazunov
Delft University of Technology Netherlands
bayesian deep learning
deep learning
robustness of deep neural networks
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Moses Chan
Northwestern University United States of America
bayesian emulation
gaussian process methods
high-dimensional statistical analysis
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Wolfgang Schief
University of New South Wales Australia
discrete differential geometry
differential geometry
general relativity
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Wei-biao Wu
University of Chicago United States of America
deep gaussian approximation
high-dimensional inference
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Aretha Teckentrup
University of Edinburgh United Kingdom
deep gaussian processes
gaussian process regression
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James Nichols
Australian National University Australia
high dimensional approximation theory
approximation
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Anastasios Kratsios
University of Toronto Canada
geometric deep learning
deep learning
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Xenia Miscouridou
Imperial College London United Kingdom
deep generative models
deep learning
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Krishna Swamy
Yale University United States of America
geometric deep learning
deep learning
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Mousumi Rizia
Australian National University Australia
deep generative modelling
deep learning
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Patryk Rygiel
University of Twente Netherlands
geometric deep learning
deep learning
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Guergana Petrova
Texas A&M University United States of America
approximation theory
nonlinear approximation
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Feng Dai
University of Alberta Canada
approximation theory
nonlinear approximation
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Jingmin Huang
University of Southampton United Kingdom
deep generative models
deep learning
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Rasmus Paulsen
Technical University of Denmark Denmark
geometric deep learning
deep reinforcement learning
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