Search Results for author: Guillaume Alain

Found 11 papers, 6 papers with code

DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop

1 code implementation10 Aug 2020 Guillaume Alain, Maxime Chevalier-Boisvert, Frederic Osterrath, Remi Piche-Taillefer

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.

Active Learning Efficient Exploration

Robo-PlaNet: Learning to Poke in a Day

no code implementations9 Nov 2019 Maxime Chevalier-Boisvert, Guillaume Alain, Florian Golemo, Derek Nowrouzezahrai

Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observations.

Model-based Reinforcement Learning reinforcement-learning

Negative eigenvalues of the Hessian in deep neural networks

no code implementations6 Feb 2019 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.

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Variance Reduction in SGD by Distributed Importance Sampling

1 code implementation20 Nov 2015 Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, Yoshua Bengio

This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient.

GSNs : Generative Stochastic Networks

no code implementations18 Mar 2015 Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, Pascal Vincent

We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood.


Techniques for Learning Binary Stochastic Feedforward Neural Networks

no code implementations11 Jun 2014 Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh

Our experiments confirm that training stochastic networks is difficult and show that the proposed two estimators perform favorably among all the five known estimators.

Structured Prediction

Deep Generative Stochastic Networks Trainable by Backprop

3 code implementations5 Jun 2013 Yoshua Bengio, Éric Thibodeau-Laufer, Guillaume Alain, Jason Yosinski

We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood.

Generalized Denoising Auto-Encoders as Generative Models

1 code implementation NeurIPS 2013 Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent

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, the reconstruction error is the squared error, and the data is continuous-valued.


What Regularized Auto-Encoders Learn from the Data Generating Distribution

no code implementations18 Nov 2012 Guillaume Alain, Yoshua Bengio

This paper clarifies some of these previous observations by showing that minimizing a particular form of regularized reconstruction error yields a reconstruction function that locally characterizes the shape of the data generating density.


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