Manzagol, Pierre-Antoine

9 publications

NeurIPS 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

ICLR 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

AISTATS 2020 On the Interplay Between Noise and Curvature and Its Effect on Optimization and Generalization Valentin Thomas, Fabian Pedregosa, Bart Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

NeurIPS 2019 Reducing the Variance in Online Optimization by Transporting past Gradients Sébastien Arnold, Pierre-Antoine Manzagol, Reza Babanezhad Harikandeh, Ioannis Mitliagkas, Nicolas Le Roux

JMLR 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

JMLR 2010 Why Does Unsupervised Pre-Training Help Deep Learning? Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio

AISTATS 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

ICML 2008 Extracting and Composing Robust Features with Denoising Autoencoders Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol

NeurIPS 2007 Topmoumoute Online Natural Gradient Algorithm Nicolas L. Roux, Pierre-antoine Manzagol, Yoshua Bengio