18 citations · 22 across the 4 of their papers we have counts for
4 papers
PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations
Louis Fortier-Dubois, Gaël Letarte, Benjamin Leblanc +2
Considering a probability distribution over parameters is known as an efficient strategy to learn a neural network with non-differentiable activation functions. We study the expect…
Out-of-distribution detection for regression tasks: parameter versus predictor entropy
Yann Pequignot, Mathieu Alain, Patrick Dallaire +4
It is crucial to detect when an instance lies downright too far from the training samples for the machine learning model to be trusted, a challenge known as out-of-distribution (OO…
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…
PAC-Bayes and Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a dif…