309 citations · 896 across the 12 of their papers we have counts for
8 papers · 1 filter
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
Guillaume Alain, Maxime Chevalier-Boisvert, Frederic Osterrath +1
DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions.…
Robo-PlaNet: Learning to Poke in a Day
Maxime Chevalier-Boisvert, Guillaume Alain, Florian Golemo +1
Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observation…
Negative eigenvalues of the Hessian in deep neural networks
Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol
The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research. In this work, we study the loss landsc…
GSNs : Generative Stochastic Networks
Guillaume Alain, Yoshua Bengio, Li Yao +4
We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based…
Deep Generative Stochastic Networks Trainable by Backprop
Yoshua Bengio, Éric Thibodeau-Laufer, Guillaume Alain +1
We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based…
Generalized Denoising Auto-Encoders as Generative Models
Yoshua Bengio, Li Yao, Guillaume Alain +1
Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian…