Graph Representation Learning Professors in Global

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C. Green
University of Wisconsin–Madison United States of America
human learning
perceptual learning
motor learning
depth learning
transfer learning
rate learning
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Danai Koutra
University of Michigan United States of America
network representation learning
graph machine learning
graph neural networks
graph mining
graph anomaly event detection
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Ehsan Mobaraki
Aalborg University Denmark
graph neural networks
knowledge representation learning
deep learning
neural-symbolic learning
knowledge graphs
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Honglak Lee
University of Michigan United States of America
representation learning
graphical models
deep learning
large-scale learning
machine learning
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Kurt Driessens
Maastricht University Netherlands
data representation
relational learning
reinforcement learning
machine learning
transfer learning
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Kurt Driessens
Maastricht University Netherlands
data representation
relational learning
reinforcement learning
machine learning
transfer learning
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Elvin Isufi
Delft University of Technology Netherlands
No active positions
graph machine learning
graph neural networks
graph signal processing
mathematical modeling
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Pan Li
Georgia Institute of Technology United States of America
graph machine learning
graph neural networks
machine learning
geometric deep learning
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Jason Lee
University of California, Berkeley United States of America
representation learning
deep learning
reinforcement learning
machine learning
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Jason Lee
University of California, Berkeley United States of America
representation learning
deep learning
reinforcement learning
machine learning
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Yaoliang Yu
University of Waterloo Canada
representation learning
distributed learning
generative modelling
machine learning
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Ferenc Huszár
University of Cambridge United Kingdom
representation learning
deep learning
machine learning
transfer learning
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Yuhong Guo
Carleton University Canada
representation learning
reinforcement learning
machine learning
self-supervised learning
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Linus Ericsson
University of Glasgow United Kingdom
representation learning
machine learning
multi-modal learning
automated machine learning
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Pieter Abbeel
University of California, Berkeley United States of America
imitation learning
deep learning
reinforcement learning
transfer learning
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Oded Yacobi
University of Sydney Australia
geometric representation theory
representation theory
categorical representation theory
combinatorial representation theory
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Krishna Swamy
Yale University United States of America
graph theory
deep learning
machine learning
geometric deep learning
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Fei Wang
Cornell University Netherlands
knowledge graphs
machine learning
transfer learning
multi-modal learning
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Yichen Zhang
Purdue University United States of America
distributed learning
reinforcement learning
transfer learning
multi-task learning
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Mehrdad Mahdavi
Pennsylvania State University United States of America
deep learning
computational learning theory
statistical learning theory
large-scale machine learning
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Shengyao Lu
University of Victoria Canada
graph representation learning
graph neural networks
reinforcement learning
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