Iterative Decoding Algorithms Professors in Global

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Federico De Martino
Maastricht University Netherlands
decoding
predictive coding
encoding
machine learning
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Mohamed Hibat-allah
University of Waterloo Canada
variational algorithms
quantum-inspired algorithms
machine learning
natural language processing
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Hazem Abbas
Queen's University Canada
evolutionary algorithms
machine learning
image processing
natural language processing
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Nicole Wein
University of Michigan United States of America
distance-estimation algorithms
parameterized algorithms
dynamic algorithms
distributed algorithms
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Jonathan Pillow
Princeton University United States of America
neural decoding
neural encoding
statistical machine learning
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John Doe
Karlsruhe Institute of Technology Germany
computational algorithms
machine learning
natural language processing
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Jonathan Shapiro
University of Manchester United Kingdom
evolutionary algorithms
machine learning
probabilistic modeling
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Nima Anari
Stanford University United States of America
sampling algorithms
parallel algorithms
algorithms
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Thomas Strohmer
University of California, Davis United States of America
numerical algorithms
machine learning
mathematical signal-image processing
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Yoshua Bengio
University of Montreal Canada
statistical learning algorithms
machine learning
natural language processing
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Gara Pruesse
Vancouver Island University Canada
approximation algorithms
discrete algorithms
optimization algorithms
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Tuğkan Batu
London School of Economics United Kingdom
sublinear algorithms
algorithms
randomized computation
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Leslie Valiant
Harvard University United States of America
holographic algorithms
computational learning
neural computation
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Jelena Diakonikolas
University of Wisconsin–Madison United States of America
first-order optimization algorithms
machine learning
large-scale optimization
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Marc Renault
University of Wisconsin United States of America
approximation algorithms
search algorithms
analysis of algorithms
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Brokoslaw Laschowski
University of Toronto Canada
brain-inspired algorithms
machine learning
neural computation
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Scott Schmidler
Duke University Singapore
high-dimensional sampling algorithms
probabilistic machine learning
bayesian computation
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Renato Monteiro
Georgia Institute of Technology United States of America
complexity algorithms
linear programming
computational optimization
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Aaron Bernstein
New York University Tandon School of Engineering United States of America
approximation algorithms
sublinear algorithms
dynamic algorithms
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Olga Taran
University of Geneva Switzerland
stochastic information processing
machine learning
image processing
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Sylvain Laizet
Imperial College London United Kingdom
quantum algorithms
machine learning
bayesian optimization
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