Yann N. Dauphin

80 papers A* 17A 1B 1Journal 42Unranked 19

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Avoiding spurious sharpness minimization broadens applicability of SAM. ICML Sidak Pal Singh, Hossein Mobahi, Atish Agarwala, Yann N. Dauphin
2025	J	`jnl`	Avoiding spurious sharpness minimization broadens applicability of SAM. CoRR Sidak Pal Singh, Hossein Mobahi, Atish Agarwala, Yann N. Dauphin
2025	J	`jnl`	Capturing Individual Human Preferences with Reward Features. CoRR André Barreto, Vincent Dumoulin, Yiran Mao, Nicolas Perez Nieves, Bobak Shahriari, Yann N. Dauphin, Doina Precup, Hugo Larochelle
2024	J	`jnl`	A density estimation perspective on learning from pairwise human preferences. Trans. Mach. Learn. Res. Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin
2024	—	`conf`	Jaxpruner: A Concise Library for Sparsity Research. CPAL 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, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J. C. Bik, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci
2024	J	`jnl`	Neglected Hessian component explains mysteries in Sharpness regularization. CoRR Yann N. Dauphin, Atish Agarwala, Hossein Mobahi
2024	A*	`conf`	Neglected Hessian component explains mysteries in sharpness regularization. NeurIPS Yann N. Dauphin, Atish Agarwala, Hossein Mobahi
2024	J	`jnl`	Towards Optimal Adapter Placement for Efficient Transfer Learning. CoRR Aleksandra Irena Nowak, Otniel-Bogdan Mercea, Anurag Arnab, Jonas Pfeiffer, Yann N. Dauphin, Utku Evci
2023	J	`jnl`	A density estimation perspective on learning from pairwise human preferences. CoRR Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin
2023	J	`jnl`	Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment. CoRR Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan
2023	J	`jnl`	JaxPruner: A concise library for sparsity research. CoRR Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan S. Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J. C. Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci
2023	J	`jnl`	Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets. CoRR Jonas Ngnawé, Marianne Abemgnigni Njifon, Jonathan Heek, Yann N. Dauphin
2023	A*	`conf`	SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. ICML Atish Agarwala, Yann N. Dauphin
2023	J	`jnl`	SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. CoRR Atish Agarwala, Yann N. Dauphin
2023	J	`jnl`	Temperature check: theory and practice for training models with softmax-cross-entropy losses. Trans. Mach. Learn. Res. Atish Agarwala, Samuel Stern Schoenholz, Jeffrey Pennington, Yann N. Dauphin
2023	A*	`conf`	Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. ICML Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk
2023	J	`jnl`	Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. CoRR Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk
2022	A*	`conf`	Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win. AAAI Utku Evci, Yani Ioannou, Cem Keskin, Yann N. Dauphin
2022	J	`jnl`	How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions? CoRR Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, Nihar B. Shah
2022	A*	`conf`	No One Representation to Rule Them All: Overlapping Features of Training Methods. ICLR Raphael Gontijo Lopes, Yann N. Dauphin, Ekin Dogus Cubuk
2021	A*	`ed.`	Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. NeurIPS Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan
2021	J	`jnl`	Auxiliary Task Update Decomposition: The Good, The Bad and The Neutral. CoRR Lucio M. Dery, Yann N. Dauphin, David Grangier
2021	A*	`conf`	Auxiliary Task Update Decomposition: the Good, the Bad and the neutral. ICLR Lucio M. Dery, Yann N. Dauphin, David Grangier
2021	J	`jnl`	Continental-Scale Building Detection from High Resolution Satellite Imagery. CoRR Wojciech Sirko, Sergii Kashubin, Marvin Ritter, Abigail Annkah, Yasser Salah Eddine Bouchareb, Yann N. Dauphin, Daniel Keysers, Maxim Neumann, Moustapha Cissé, John Quinn
2021	A*	`conf`	Deconstructing the Regularization of BatchNorm. ICLR Yann N. Dauphin, Ekin Dogus Cubuk
2021	J	`jnl`	No One Representation to Rule Them All: Overlapping Features of Training Methods. CoRR Raphael Gontijo Lopes, Yann N. Dauphin, Ekin D. Cubuk
2020	J	`jnl`	Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win. CoRR Utku Evci, Yani Andrew Ioannou, Cem Keskin, Yann N. Dauphin
2020	—	`conf`	Robust and On-the-Fly Dataset Denoising for Image Classification. ECCV (29) Jiaming Song, Yann N. Dauphin, Michael Auli, Tengyu Ma
2020	J	`jnl`	Robust and On-the-fly Dataset Denoising for Image Classification. CoRR Jiaming Song, Lunjia Hu, Yann N. Dauphin, Michael Auli, Tengyu Ma
2020	J	`jnl`	Temperature check: theory and practice for training models with softmax-cross-entropy losses. CoRR Atish Agarwala, Jeffrey Pennington, Yann N. Dauphin, Samuel S. Schoenholz
2019	—	`conf`	Fixup Initialization: Residual Learning Without Normalization. ICLR (Poster) Hongyi Zhang, Yann N. Dauphin, Tengyu Ma
2019	J	`jnl`	Fixup Initialization: Residual Learning Without Normalization. CoRR Hongyi Zhang, Yann N. Dauphin, Tengyu Ma
2019	A*	`conf`	MetaInit: Initializing learning by learning to initialize. NeurIPS Yann N. Dauphin, Samuel S. Schoenholz
2019	A	`conf`	On the Pitfalls of Measuring Emergent Communication. AAMAS Ryan Lowe, Jakob N. Foerster, Y-Lan Boureau, Joelle Pineau, Yann N. Dauphin
2019	J	`jnl`	On the Pitfalls of Measuring Emergent Communication. CoRR Ryan Lowe, Jakob N. Foerster, Y-Lan Boureau, Joelle Pineau, Yann N. Dauphin
2019	—	`conf`	Pay Less Attention with Lightweight and Dynamic Convolutions. ICLR Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, Michael Auli
2019	J	`jnl`	Pay Less Attention with Lightweight and Dynamic Convolutions. CoRR Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, Michael Auli
2019	J	`jnl`	Selective Brain Damage: Measuring the Disparate Impact of Model Pruning. CoRR Sara Hooker, Aaron C. Courville, Yann N. Dauphin, Andrea Frome
2019	—	`conf`	Simple and Effective Noisy Channel Modeling for Neural Machine Translation. EMNLP/IJCNLP (1) Kyra Yee, Yann N. Dauphin, Michael Auli
2019	J	`jnl`	Simple and Effective Noisy Channel Modeling for Neural Machine Translation. CoRR Kyra Yee, Nathan Ng, Yann N. Dauphin, Michael Auli
2019	—	`conf`	Strategies for Structuring Story Generation. ACL (1) Angela Fan, Mike Lewis, Yann N. Dauphin
2019	J	`jnl`	Strategies for Structuring Story Generation. CoRR Angela Fan, Mike Lewis, Yann N. Dauphin
2018	—	`conf`	Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. ICLR (Workshop) Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou
2018	—	`conf`	Hierarchical Neural Story Generation. ACL (1) Angela Fan, Mike Lewis, Yann N. Dauphin
2018	J	`jnl`	Hierarchical Neural Story Generation. CoRR Angela Fan, Mike Lewis, Yann N. Dauphin
2018	—	`conf`	mixup: Beyond Empirical Risk Minimization. ICLR (Poster) Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz
2017	—	`conf`	A Convolutional Encoder Model for Neural Machine Translation. ACL (1) Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin
2017	A*	`conf`	Convolutional Sequence to Sequence Learning. ICML Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
2017	J	`jnl`	Convolutional Sequence to Sequence Learning. CoRR Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
2017	J	`jnl`	Deal or No Deal? End-to-End Learning for Negotiation Dialogues. CoRR Mike Lewis, Denis Yarats, Yann N. Dauphin, Devi Parikh, Dhruv Batra
2017	A*	`conf`	Deal or No Deal? End-to-End Learning of Negotiation Dialogues. EMNLP Mike Lewis, Denis Yarats, Yann N. Dauphin, Devi Parikh, Dhruv Batra
2017	J	`jnl`	Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. CoRR Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou
2017	A*	`conf`	Language Modeling with Gated Convolutional Networks. ICML Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier
2017	A*	`conf`	Parseval Networks: Improving Robustness to Adversarial Examples. ICML Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann N. Dauphin, Nicolas Usunier
2017	J	`jnl`	Parseval Networks: Improving Robustness to Adversarial Examples. CoRR Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann N. Dauphin, Nicolas Usunier
2017	J	`jnl`	Tackling Over-pruning in Variational Autoencoders. CoRR Serena Yeung, Anitha Kannan, Yann N. Dauphin, Li Fei-Fei
2017	J	`jnl`	mixup: Beyond Empirical Risk Minimization. CoRR Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz
2016	J	`jnl`	A Convolutional Encoder Model for Neural Machine Translation. CoRR Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin
2016	J	`jnl`	EmoNets: Multimodal deep learning approaches for emotion recognition in video. J. Multimodal User Interfaces 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, David Warde-Farley, Aaron C. Courville, Pascal Vincent, Roland Memisevic, Christopher Joseph Pal, Yoshua Bengio
2016	J	`jnl`	Language Modeling with Gated Convolutional Networks. CoRR Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier
2016	A*	`conf`	Predicting distributions with Linearizing Belief Networks. ICLR (Poster) Yann N. Dauphin, David Grangier
2016	J	`jnl`	Theano: A Python framework for fast computation of mathematical expressions. CoRR 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
2015	J	`jnl`	EmoNets: Multimodal deep learning approaches for emotion recognition in video. CoRR Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin, Çaglar Gülçehre, Vincent Michalski, Kishore Reddy Konda, Sébastien Jean, Pierre Froumenty, Yann N. Dauphin, Nicolas Boulanger-Lewandowski, Raul Chandias Ferrari, Mehdi Mirza, David Warde-Farley, Aaron C. Courville, Pascal Vincent, Roland Memisevic, Christopher J. Pal, Yoshua Bengio
2015	—	`conf`	Equilibrated adaptive learning rates for non-convex optimization. NIPS Yann N. Dauphin, Harm de Vries, Yoshua Bengio
2015	J	`jnl`	RMSProp and equilibrated adaptive learning rates for non-convex optimization. CoRR Yann N. Dauphin, Harm de Vries, Junyoung Chung, Yoshua Bengio
2015	J	`jnl`	Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding. IEEE ACM Trans. Audio Speech Lang. Process. Grégoire Mesnil, Yann N. Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tür, Xiaodong He, Larry P. Heck, Gökhan Tür, Dong Yu, Geoffrey Zweig
2014	—	`conf`	Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. NIPS Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
2014	J	`jnl`	Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. CoRR Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
2014	J	`jnl`	On the saddle point problem for non-convex optimization. CoRR Razvan Pascanu, Yann N. Dauphin, Surya Ganguli, Yoshua Bengio
2014	—	`conf`	Zero-Shot Learning and Clustering for Semantic Utterance Classification. ICLR (Poster) Yann N. Dauphin, Gökhan Tür, Dilek Hakkani-Tür, Larry P. Heck
2013	—	`conf`	Better Mixing via Deep Representations. ICML (1) Yoshua Bengio, Grégoire Mesnil, Yann N. Dauphin, Salah Rifai
2013	—	`conf`	Big Neural Networks Waste Capacity ICLR (Workshop) Yann N. Dauphin, Yoshua Bengio
2013	B	`conf`	Combining modality specific deep neural networks for emotion recognition in video. ICMI 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, Pierre Luc Carrier, Yann N. Dauphin, Nicolas Boulanger-Lewandowski, Abhishek Aggarwal, Jeremie Zumer, Pascal Lamblin, Jean-Philippe Raymond, Guillaume Desjardins, Razvan Pascanu, David Warde-Farley, Atousa Torabi, Arjun Sharma, Emmanuel Bengio, Kishore Reddy Konda, Zhenzhou Wu
2013	—	`conf`	Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs. NIPS Yann N. Dauphin, Yoshua Bengio
2012	A*	`conf`	A Generative Process for Contractive Auto-Encoders. ICML Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio
2012	J	`jnl`	Better Mixing via Deep Representations CoRR Yoshua Bengio, Grégoire Mesnil, Yann N. Dauphin, Salah Rifai
2012	—	`conf`	Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. ICML Unsupervised and Transfer Learning 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, James Bergstra
2011	—	`conf`	Higher Order Contractive Auto-Encoder. ECML/PKDD (2) Salah Rifai, Grégoire Mesnil, Pascal Vincent, Xavier Muller, Yoshua Bengio, Yann N. Dauphin, Xavier Glorot
2011	A*	`conf`	Large-Scale Learning of Embeddings with Reconstruction Sampling. ICML Yann N. Dauphin, Xavier Glorot, Yoshua Bengio
2011	—	`conf`	The Manifold Tangent Classifier. NIPS Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller