Topmoumoute Online Natural Gradient Algorithm. - Nicolas Le Roux; Pierre-Antoine Manzagol; Yoshua Bengio (NIPS, 2007)

Extracting and composing robust features with denoising autoencoders. - Pascal Vincent; Hugo Larochelle; Yoshua Bengio; Pierre-Antoine Manzagol (ICML, 2008)

On the Use of Sparce Time Relative Auditory Codes for Music. - Pierre-Antoine Manzagol; Thierry Bertin-Mahieux; Douglas Eck (ISMIR, 2008)

The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training. - Dumitru Erhan; Pierre-Antoine Manzagol; Yoshua Bengio; Samy Bengio; Pascal Vincent (AISTATS, 2009)

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)

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)

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; et al. (CoRR, 2016)

Negative eigenvalues of the Hessian in deep neural networks. - Guillaume Alain; Nicolas Le Roux; Pierre-Antoine Manzagol (ICLR (Workshop), 2018)

Online variance-reducing optimization. - Nicolas Le Roux; Reza Babanezhad 001; Pierre-Antoine Manzagol (ICLR (Workshop), 2018)

Reducing the variance in online optimization by transporting past gradients. - Sébastien M. R. Arnold; Pierre-Antoine Manzagol; Reza Babanezhad 001; Ioannis Mitliagkas; Nicolas Le Roux (NeurIPS, 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)

Information matrices and generalization. - Valentin Thomas; Fabian Pedregosa; Bart van Merriënboer; Pierre-Antoine Manzagol; Yoshua Bengio; Nicolas Le Roux (CoRR, 2019)

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 (AISTATS, 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 (ICLR, 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 (ICSE (Workshops), 2020)

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 (NeurIPS, 2021)

Resolving Code Review Comments with Machine Learning. - 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; et al. (ICSE-SEIP, 2024)