Neural Network Interpretability Professors in Global

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Matthias Wilms
University of Michigan United States of America
interpretability
explainability
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Michael Guerzhoy
University of Toronto Canada
mechanistic interpretability
interpretable machine learning
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Fei Wang
Cornell University Netherlands
model interpretability
explainability
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Fei Wang
Cornell University Netherlands
model interpretability
explainability
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Amir Rahnama
KTH Royal Institute of Technology Sweden
interpretable machine learning
explainable machine learning
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Zihuai University
Stanford University United States of America
interpretable machine learning
explainable machine learning
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Christopher Manning
Stanford University United States of America
neural machine translation
natural language understanding
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Ehsan Mobaraki
Aalborg University Denmark
explainability
explainable artificial intelligence
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Helle Dam
Aarhus University Denmark
interpretation theory
consecutive interpreting
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Tim Stowell
University of California Los Angeles United States of America
semantic interpretation
temporal interpretation
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Chris Eliasmith
University of Waterloo Canada
neural computing
neural networks
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Andre Freitas
University of Manchester United Kingdom
natural language inference
explainable ai
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Sandra Hale
University of New South Wales Australia
court interpreting
legal interpreting
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Brokoslaw Laschowski
University of Toronto Canada
neural computation
neural networks
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Sun-yuan Kung
Princeton University United States of America
neural networks
explainable ai
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Brent Doiron
University of Chicago United States of America
network learning
neural coding
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Brent Doiron
University of Chicago United States of America
network learning
neural coding
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Jonathan Pillow
Princeton University United States of America
neural encoding
neural decoding
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Umut Güçlü
Radboud University Netherlands
neural encoding
neural decoding
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Yunyao Cheng
Aalborg University Denmark
neural network interpretability
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Alejandro Hernandez
Stockholm University Sweden
machine learning interpretability
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