Sequence Learning Professors in Global

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Yuhong Guo
Carleton University Canada
machine learning
self-supervised learning
representation learning
semi-supervised learning
reinforcement learning
continual learning
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John Oommen
Carleton University Canada
stochastic learning
pattern recognition
statistical pattern recognition
learning systems
syntactic pattern recognition
neural networks
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C. Green
University of Wisconsin–Madison United States of America
human learning
motor learning
depth learning
perceptual learning
cognitive learning
transfer learning
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Pascal Vincent
University of Montreal Canada
machine learning
deep learning
pattern recognition
representation learning
neural networks
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Rudolf Lioutikov
University of Texas at Austin United States of America
sequencing
imitation learning
structure learning
reinforcement learning
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Daniel Roy
University of Toronto Canada
No active positions
machine learning
deep learning
sequential decision making
statistical learning theory
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Honglak Lee
University of Michigan United States of America
machine learning
deep learning
large-scale learning
representation learning
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Kin-choong Yow
University of Regina Canada
machine learning
deep learning
pattern recognition
deep reinforcement learning
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Jason Lee
University of California, Berkeley United States of America
machine learning
deep learning
representation learning
reinforcement learning
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Yee Teh
University of Oxford United Kingdom
machine learning
deep learning
probabilistic learning
large scale machine learning
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Jason Lee
University of California, Berkeley United States of America
machine learning
deep learning
representation learning
reinforcement learning
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Siamak Mehrkanoon
Utrecht University Netherlands
deep learning
pattern recognition
semi-supervised learning
neural networks
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Alexander Rakhlin
Massachusetts Institute of Technology United States of America
machine learning
statistical learning
reinforcement learning
neural networks
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Vinay Gogineni
University of Southern Denmark Denmark
machine learning
self-supervised learning
quantum machine learning
continual learning
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Ferenc Huszár
University of Cambridge United Kingdom
machine learning
deep learning
representation learning
transfer learning
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Guilherme Dinis Chaliane Junior
Stockholm University Sweden
machine learning
sequential decision making
representation learning
reinforcement learning
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Ramandeep Randhawa
University of Southern California United States of America
machine learning
deep learning
stochastic modeling
queueing theory
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Pieter Abbeel
University of California, Berkeley United States of America
deep learning
imitation learning
reinforcement learning
transfer learning
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Brokoslaw Laschowski
University of Toronto Canada
machine learning
deep learning
reinforcement learning
neural networks
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Mehrdad Mahdavi
Pennsylvania State University United States of America
deep learning
statistical learning theory
computational learning theory
large-scale machine learning
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Kurt Driessens
Maastricht University Netherlands
machine learning
relational learning
reinforcement learning
transfer learning
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