Large-Scale Learning of Embeddings with Reconstruction Sampling. - Yann N. Dauphin; Xavier Glorot; Yoshua Bengio (ICML, 2011)

The Manifold Tangent Classifier. - Salah Rifai; Yann N. Dauphin; Pascal Vincent; Yoshua Bengio; Xavier Muller (NIPS, 2011)

Higher Order Contractive Auto-Encoder. - Salah Rifai; Grégoire Mesnil; Pascal Vincent; Xavier Muller; Yoshua Bengio; Yann N. Dauphin; Xavier Glorot (ECML/PKDD (2), 2011)

A Generative Process for Contractive Auto-Encoders. - Salah Rifai; Yann N. Dauphin; Pascal Vincent; Yoshua Bengio (ICML, 2012)

Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. - Grégoire Mesnil; Yann N. Dauphin; Xavier Glorot; Salah Rifai; Yoshua Bengio; Ian J. Goodfellow; Erick Lavoie; Xavier Muller; Guillaume Desjardins; David Warde-Farley; Pascal Vincent; Aaron C. Courville; et al. (ICML Unsupervised and Transfer Learning, 2012)

Combining modality specific deep neural networks for emotion recognition in video. - Samira Ebrahimi Kahou; Christopher J. Pal; Xavier Bouthillier; Pierre Froumenty; Çaglar Gülçehre; Roland Memisevic; Pascal Vincent; Aaron C. Courville; Yoshua Bengio; Raul Chandias Ferrari; Mehdi Mirza; Sébastien Jean; et al. (ICMI, 2013)

Better Mixing via Deep Representations. - Yoshua Bengio; Grégoire Mesnil; Yann N. Dauphin; Salah Rifai (ICML (1), 2013)

Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs. - Yann N. Dauphin; Yoshua Bengio (NIPS, 2013)

Big Neural Networks Waste Capacity - Yann N. Dauphin; Yoshua Bengio (ICLR (Workshop), 2013)

Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. - Yann N. Dauphin; Razvan Pascanu; Çaglar Gülçehre; KyungHyun Cho; Surya Ganguli; Yoshua Bengio (NIPS, 2014)

Zero-Shot Learning and Clustering for Semantic Utterance Classification. - Yann N. Dauphin; Gökhan Tür; Dilek Hakkani-Tür; Larry P. Heck (ICLR (Poster), 2014)

On the saddle point problem for non-convex optimization. - Razvan Pascanu; Yann N. Dauphin; Surya Ganguli; Yoshua Bengio (CoRR, 2014)

Equilibrated adaptive learning rates for non-convex optimization. - Yann N. Dauphin; Harm de Vries; Yoshua Bengio (NIPS, 2015)

RMSProp and equilibrated adaptive learning rates for non-convex optimization. - Yann N. Dauphin; Harm de Vries; Junyoung Chung; Yoshua Bengio (CoRR, 2015)

Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding. - Grégoire Mesnil; Yann N. Dauphin; Kaisheng Yao; Yoshua Bengio; Li Deng 001; Dilek Hakkani-Tür; Xiaodong He 001; Larry P. Heck; Gökhan Tür; Dong Yu 001; Geoffrey Zweig (IEEE ACM Trans. Audio Speech Lang. Process., 2015)

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)

Predicting distributions with Linearizing Belief Networks. - Yann N. Dauphin; David Grangier (ICLR (Poster), 2016)

EmoNets: Multimodal deep learning approaches for emotion recognition in video. - Samira Ebrahimi Kahou; Xavier Bouthillier; Pascal Lamblin; Çaglar Gülçehre; Vincent Michalski; Kishore Konda; Sébastien Jean; Pierre Froumenty; Yann N. Dauphin; Nicolas Boulanger-Lewandowski; Raul Chandias Ferrari; Mehdi Mirza; et al. (J. Multimodal User Interfaces, 2016)

A Convolutional Encoder Model for Neural Machine Translation. - Jonas Gehring; Michael Auli; David Grangier; Yann N. Dauphin (ACL (1), 2017)

Deal or No Deal? End-to-End Learning of Negotiation Dialogues. - Mike Lewis; Denis Yarats; Yann N. Dauphin; Devi Parikh; Dhruv Batra (EMNLP, 2017)

Parseval Networks: Improving Robustness to Adversarial Examples. - Moustapha Cissé; Piotr Bojanowski; Edouard Grave; Yann N. Dauphin; Nicolas Usunier (ICML, 2017)

Language Modeling with Gated Convolutional Networks. - Yann N. Dauphin; Angela Fan; Michael Auli; David Grangier (ICML, 2017)

Convolutional Sequence to Sequence Learning. - Jonas Gehring; Michael Auli; David Grangier; Denis Yarats; Yann N. Dauphin (ICML, 2017)

Tackling Over-pruning in Variational Autoencoders. - Serena Yeung; Anitha Kannan; Yann N. Dauphin; Li Fei-Fei 001 (CoRR, 2017)

Hierarchical Neural Story Generation. - Angela Fan; Mike Lewis; Yann N. Dauphin (ACL (1), 2018)

Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. - Levent Sagun; Utku Evci; V. Ugur Güney; Yann N. Dauphin; Léon Bottou (ICLR (Workshop), 2018)

mixup: Beyond Empirical Risk Minimization. - Hongyi Zhang; Moustapha Cissé; Yann N. Dauphin; David Lopez-Paz (ICLR (Poster), 2018)

Strategies for Structuring Story Generation. - Angela Fan; Mike Lewis; Yann N. Dauphin (ACL (1), 2019)

On the Pitfalls of Measuring Emergent Communication. - Ryan Lowe; Jakob N. Foerster; Y-Lan Boureau; Joelle Pineau; Yann N. Dauphin (AAMAS, 2019)

Simple and Effective Noisy Channel Modeling for Neural Machine Translation. - Kyra Yee; Yann N. Dauphin; Michael Auli (EMNLP/IJCNLP (1), 2019)

Pay Less Attention with Lightweight and Dynamic Convolutions. - Felix Wu; Angela Fan; Alexei Baevski; Yann N. Dauphin; Michael Auli (ICLR, 2019)

Fixup Initialization: Residual Learning Without Normalization. - Hongyi Zhang; Yann N. Dauphin; Tengyu Ma 001 (ICLR (Poster), 2019)

MetaInit: Initializing learning by learning to initialize. - Yann N. Dauphin; Samuel S. Schoenholz (NeurIPS, 2019)

Simple and Effective Noisy Channel Modeling for Neural Machine Translation. - Kyra Yee; Nathan Ng 001; Yann N. Dauphin; Michael Auli (CoRR, 2019)

Selective Brain Damage: Measuring the Disparate Impact of Model Pruning. - Sara Hooker; Aaron C. Courville; Yann N. Dauphin; Andrea Frome (CoRR, 2019)

Robust and On-the-Fly Dataset Denoising for Image Classification. - Jiaming Song; Yann N. Dauphin; Michael Auli; Tengyu Ma 001 (ECCV (29), 2020)

Robust and On-the-fly Dataset Denoising for Image Classification. - Jiaming Song; Lunjia Hu; Yann N. Dauphin; Michael Auli; Tengyu Ma 001 (CoRR, 2020)

Deconstructing the Regularization of BatchNorm. - Yann N. Dauphin; Ekin Dogus Cubuk (ICLR, 2021)

Auxiliary Task Update Decomposition: the Good, the Bad and the neutral. - Lucio M. Dery; Yann N. Dauphin; David Grangier (ICLR, 2021)

Continental-Scale Building Detection from High Resolution Satellite Imagery. - Wojciech Sirko; Sergii Kashubin; Marvin Ritter; Abigail Annkah; Yasser Salah Eddine Bouchareb; Yann N. Dauphin; Daniel Keysers; Maxim Neumann; Moustapha Cissé; John Quinn (CoRR, 2021)

Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win. - Utku Evci; Yani Ioannou; Cem Keskin; Yann N. Dauphin (AAAI, 2022)

No One Representation to Rule Them All: Overlapping Features of Training Methods. - Raphael Gontijo Lopes; Yann N. Dauphin; Ekin Dogus Cubuk (ICLR, 2022)

How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions? - Charvi Rastogi; Ivan Stelmakh; Alina Beygelzimer; Yann N. Dauphin; Percy Liang; Jennifer Wortman Vaughan; Zhenyu Xue; Hal Daumé III; Emma Pierson; Nihar B. Shah (CoRR, 2022)

SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. - Atish Agarwala; Yann N. Dauphin (ICML, 2023)

Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. - Emirhan Kurtulus; Zichao Li 002; Yann N. Dauphin; Ekin Dogus Cubuk (ICML, 2023)

Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets. - Jonas Ngnawé; Marianne Abemgnigni Njifon; Jonathan Heek; Yann N. Dauphin (CoRR, 2023)

Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment. - Alina Beygelzimer; Yann N. Dauphin; Percy Liang; Jennifer Wortman Vaughan (CoRR, 2023)

Temperature check: theory and practice for training models with softmax-cross-entropy losses. - Atish Agarwala; Samuel Stern Schoenholz; Jeffrey Pennington; Yann N. Dauphin (Trans. Mach. Learn. Res., 2023)

Jaxpruner: A Concise Library for Sparsity Research. - Joo Hyung Lee; Wonpyo Park; Nicole Mitchell; Jonathan Pilault; Johan Samir Obando Ceron; Han-Byul Kim; Namhoon Lee; Elias Frantar; Yun Long; Amir Yazdanbakhsh; Woohyun Han; Shivani Agrawal; et al. (CPAL, 2024)

Neglected Hessian component explains mysteries in sharpness regularization. - Yann N. Dauphin; Atish Agarwala; Hossein Mobahi (NeurIPS, 2024)

Towards Optimal Adapter Placement for Efficient Transfer Learning. - Aleksandra Irena Nowak; Otniel-Bogdan Mercea; Anurag Arnab; Jonas Pfeiffer; Yann N. Dauphin; Utku Evci (CoRR, 2024)

A density estimation perspective on learning from pairwise human preferences. - Vincent Dumoulin; Daniel D. Johnson 001; Pablo Samuel Castro; Hugo Larochelle; Yann N. Dauphin (Trans. Mach. Learn. Res., 2024)

Avoiding spurious sharpness minimization broadens applicability of SAM. - Sidak Pal Singh; Hossein Mobahi; Atish Agarwala; Yann N. Dauphin (CoRR, 2025)

Capturing Individual Human Preferences with Reward Features. - André Barreto 001; Vincent Dumoulin; Yiran Mao; Nicolas Perez Nieves; Bobak Shahriari; Yann N. Dauphin; Doina Precup; Hugo Larochelle (CoRR, 2025)