Sparse Learning Professors in Global

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Yuhong Guo
Carleton University Canada
self-supervised learning
semi-supervised learning
unsupervised learning
few-shot learning
weakly supervised learning
representation learning
active learning
federated learning
continual learning
machine learning
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C. Green
University of Wisconsin–Madison United States of America
human learning
perceptual learning
motor learning
transfer learning
depth learning
cognitive learning
rate learning
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Yuri Boykov
University of Waterloo Canada
self-supervised learning
semi-supervised learning
unsupervised learning
weakly supervised learning
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Samory Kpotufe
Columbia University United States of America
transfer learning
adaptive learning
statistical machine learning
active learning
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Ruth Urner
York University Canada
semi-supervised learning
transfer learning
active learning
machine learning
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Il-min Kim
Queen's University Canada
distributed learning
few-shot learning
federated learning
continual learning
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Pieter Abbeel
University of California, Berkeley United States of America
imitation learning
transfer learning
unsupervised learning
deep learning
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Siamak Mehrkanoon
Utrecht University Netherlands
semi-supervised learning
unsupervised learning
deep learning
pattern recognition
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Yaoliang Yu
University of Waterloo Canada
distributed learning
representation learning
federated learning
machine learning
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Konstantinos Kamnitsas
University of Oxford United Kingdom
transfer learning
deep learning
federated learning
machine learning
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Randall Balestriero
Brown University United States of America
self-supervised learning
deep learning
representation learning
learnable signal processing
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Ferenc Huszár
University of Cambridge United Kingdom
transfer learning
deep learning
representation learning
machine learning
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Mengye Ren
New York University United States of America
few-shot learning
representation learning
continual learning
machine learning
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Honglak Lee
University of Michigan United States of America
large-scale learning
deep learning
representation learning
machine learning
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Oisin Mac Aodha
University of Edinburgh United Kingdom
deep learning
active learning
machine teaching
machine learning
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Arthur Galamba
King's College London United Kingdom
collaborative learning
hybrid learning
online learning
stem learning
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Fei Wang
Cornell University Netherlands
transfer learning
federated learning
multi-modal learning
machine learning
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Linus Ericsson
University of Glasgow United Kingdom
representation learning
multi-modal learning
automated machine learning
machine learning
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Pascal Vincent
University of Montreal Canada
deep learning
representation learning
machine learning
pattern recognition
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Guillaume Obozinski
École Polytechnique Fédérale de Lausanne Switzerland
sparse modeling
relational learning
machine learning
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Irina Rish
University of Montreal Canada
sparse modeling
deep learning
machine learning
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