2 papers
cs.LG2019
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Gaël Letarte, Pascal Germain, Benjamin Guedj +1
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-e…
stat.ML2018
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
Gaël Letarte, Emilie Morvant, Pascal Germain
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 kern…