194 citations · 543 across the 29 of their papers we have counts for
5 papers · 1 filter
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma +4
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on pa…
Uncertainty in Multitask Transfer Learning
Alexandre Lacoste, Boris Oreshkin, Wonchang Chung +3
Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks. The result is a rich and meaningful p…
Deep Prior
Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh +3
The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds. In this work we investigate th…
Bayesian Hypernetworks
David Krueger, Chin-Wei Huang, Riashat Islam +3
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork $\h$ is a neural network which learns to transform a sim…
PAC-Bayesian Theory Meets Bayesian Inference
Pascal Germain, Francis Bach, Alexandre Lacoste +1
We exhibit a strong link between frequentist PAC-Bayesian risk bounds and the Bayesian marginal likelihood. That is, for the negative log-likelihood loss function, we show that the…