Uncertainty Deep Learning Professors in Global

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Samir Maity
Aalborg University Denmark
uncertainty quantification
deep learning
machine learning
imprecise modeling
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Yarin Gal
University of Oxford United Kingdom
deep reinforcement learning
deep probabilistic ai
bayesian deep learning
machine learning
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Yarin Gal
University of Oxford United Kingdom
deep reinforcement learning
deep probabilistic ai
bayesian deep learning
machine learning
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Mikhail Glazunov
Delft University of Technology Netherlands
deep learning
epistemic uncertainty
robustness of deep neural networks
bayesian deep learning
unsupervised anomaly detection
unsupervised learning
probabilistic modeling
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Ben Adcock
Simon Fraser University Canada
uncertainty quantification
deep learning
machine learning
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John Quilty
University of Waterloo Canada
uncertainty quantification
deep learning
machine learning
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Agustinus Kristiadi
Western University Canada
uncertainty quantification
deep learning
probabilistic machine learning
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Robert Gracie
University of Waterloo Canada
deep learning
uncertainty
machine learning
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Geoff Pleiss
University of British Columbia Canada
uncertainty quantification
deep learning
probabilistic modeling
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Kin-choong Yow
University of Regina Canada
deep learning
deep reinforcement learning
machine learning
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Justin Beland
University of Toronto Canada
deep learning
optimization under uncertainty
probabilistic modeling
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Jingmin Huang
University of Southampton United Kingdom
deep learning
deep generative models
machine learning
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Krishna Swamy
Yale University United States of America
deep learning
geometric deep learning
machine learning
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Martin Trapp
KTH Royal Institute of Technology Sweden
uncertainty quantification
bayesian deep learning
probabilistic machine learning
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Christoph Brune
University of Twente Netherlands
deep learning
geometric deep learning
deep gaussian processes
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Michele Caprio
University of Manchester United Kingdom
uncertainty quantification
machine learning
imprecise probabilistic machine learning
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Yingzhen Li
Imperial College London United Kingdom
uncertainty quantification
machine learning
probabilistic machine learning
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Magnus Ross
University College London United Kingdom
uncertainty quantification
machine learning
probabilistic machine learning
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Geoff Pleiss
University of British Columbia Canada
uncertainty quantification
reliable deep learning
machine learning
probabilistic machine learning
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Irina Rish
University of Montreal Canada
deep learning
probabilistic inference
machine learning
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Felix Agyemang
University of Manchester United Kingdom
deep learning
machine learning
neural networks
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