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Gaël Letarte
Gaël Letarte
Mathematician • Machine Learning Researcher
Short bio
I am currently a Ph.D. student in Machine Learning at Université Laval, supervised by Pascal Germain and François Laviolette. I am a founding member of Baseline, a cooperative of data scientists.
Publications
Conference Papers
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Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette (NeurIPS 2019).
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks .
[ pdf ] • [ link ] • [ GitHub ] • [ poster ] • [ Youtube ] -
Gaël Letarte, Emilie Morvant, Pascal Germain (AISTATS 2019).
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior .
[ pdf ] • [ link ] • [ GitHub ] • [ poster ]
Journal Papers
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Alexandre Drouin, Gaël Letarte, Frédéric Raymond, Mario Marchand, Jacques Corbeil, François Laviolette (Scientific Report 2019).
Interpretable genotype-to-phenotype classifiers with performance guarantees.
[ pdf ] • [ supp ] • [ link ] • [ GitHub ]
Workshops
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Gaël Letarte, Frédérik Paradis, Philippe Giguère, François Laviolette (EMNLP 2018).
Importance of Self-Attention for Sentiment Analysis.
[ pdf ] • [ link ] • [ GitHub ] • [ poster ] -
Alexandre Drouin, Frédéric Raymond, Gaël Letarte, Mario Marchand, Jacques Corbeil, François Laviolette (NIPS 2016).
Large scale modeling of antimicrobial resistance with interpretable classifiers.
[ pdf ] • [ link ] • [ GitHub ]
Projects
Tool allowing to learn interpretable computational phenotyping models from k-merized genomic data.
Cooperative of data scientists operating in Quebec City and striving to democratize artificial intelligence.
A Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.
Academic Affiliations
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- © Gaël Letarte, 2019
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