Pierre-Antoine Manzagol

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 14

Top Topics

Top Venues

ICLR (Workshop)

Publications

Alexander Frömmgen, Jacob Austin, Peter Choy, Nimesh Ghelani, Lera Kharatyan, Gabriela Surita, Elena Khrapko, Pascal Lamblin, Pierre-Antoine Manzagol, Marcus Revaj, Maxim Tabachnyk, Daniel Tarlow, Kevin Villela, Daniel Zheng, Satish Chandra, Petros Maniatis
Resolving Code Review Comments with Machine Learning. ICSE-SEIP (2024)
Zimin Chen, Vincent J. Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair. NeurIPS (2021)
Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux
On the interplay between noise and curvature and its effect on optimization and generalization. AISTATS (2020)
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. ICLR (2020)
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
Learning to Fix Build Errors with Graph2Diff Neural Networks. ICSE (Workshops) (2020)
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
Learning to Fix Build Errors with Graph2Diff Neural Networks. CoRR (2019)
Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux
Information matrices and generalization. CoRR (2019)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. NeurIPS (2019)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. CoRR (2019)
Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol
Negative eigenvalues of the Hessian in deep neural networks. CoRR (2019)
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. CoRR (2019)
Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol
Online variance-reducing optimization. ICLR (Workshop) (2018)
Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol
Negative eigenvalues of the Hessian in deep neural networks. ICLR (Workshop) (2018)
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
Theano: A Python framework for fast computation of mathematical expressions. CoRR (2016)
Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio
Why Does Unsupervised Pre-training Help Deep Learning? J. Mach. Learn. Res. 11 (2010)
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J. Mach. Learn. Res. 11 (2010)
Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, Pascal Vincent
The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training. AISTATS (2009)
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol
Extracting and composing robust features with denoising autoencoders. ICML (2008)
Pierre-Antoine Manzagol, Thierry Bertin-Mahieux, Douglas Eck
On the Use of Sparce Time Relative Auditory Codes for Music. ISMIR (2008)
Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio
Topmoumoute Online Natural Gradient Algorithm. NIPS (2007)