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Gaël Letarte

2 accepted papers

2019

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

NeurIPS 2019poster

We present a comprehensive study of multilayer neural networks with binary activation, relying on the PAC-Bayesian theory. Our contributions are twofold: (i) we develop an end-to-end framework to train a binary activated deep neural network, (ii) we provide nonvacuous PAC-Bayesian generalization bou…

2019

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior

AISTATS 2019poster

We revisit Rahimi and Recht (2007)’s kernel random Fourier features (RFF) method through the lens of the PAC-Bayesian theory. While the primary goal of RFF is to approximate a kernel, we look at the Fourier transform as a prior distribution over trigonometric hypotheses. It naturally suggests learni…