Pierre-Antoine Manzagol

According to our database, Pierre-Antoine Manzagol authored at least 16 papers between 2007 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2021

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair.

Zimin Chen, Vincent J. Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Learning to Fix Build Errors with Graph2Diff Neural Networks.

Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian

Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Workshops, Seoul, Republic of Korea, 27 June, 2020

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples.

Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle

Proceedings of the 8th International Conference on Learning Representations, 2020

On the interplay between noise and curvature and its effect on optimization and generalization.

Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Information matrices and generalization.

Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

CoRR, 2019

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples.

Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle

CoRR, 2019

Reducing the variance in online optimization by transporting past gradients.

Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Online variance-reducing optimization.

Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol

Proceedings of the 6th International Conference on Learning Representations, 2018

Negative eigenvalues of the Hessian in deep neural networks.

Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Theano: A Python framework for fast computation of mathematical expressions.

Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, 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 F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, 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 Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph 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 P. 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

CoRR, 2016

2010

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion.

Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol

J. Mach. Learn. Res., 2010

Why Does Unsupervised Pre-training Help Deep Learning?

Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio

J. Mach. Learn. Res., 2010

2009

The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training.

Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, Pascal Vincent

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

2008

On the Use of Sparce Time Relative Auditory Codes for Music.

Pierre-Antoine Manzagol, Thierry Bertin-Mahieux, Douglas Eck

Proceedings of the ISMIR 2008, 2008

Extracting and composing robust features with denoising autoencoders.

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol

Proceedings of the Machine Learning, 2008

2007

Topmoumoute Online Natural Gradient Algorithm.

Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 20, 2007