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ORCIDYann N. Dauphin
Person information
- affiliation: Google AI, Accra, Ghana
- affiliation: Facebook AI Research, Menlo Park, CA, USA
- affiliation: University of Montréal, Department of Computer Science and Operations Research, Canada
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Journal Articles
- 2023
[j3]Atish Agarwala, Samuel Stern Schoenholz, Jeffrey Pennington, Yann N. Dauphin:
Temperature check: theory and practice for training models with softmax-cross-entropy losses. Trans. Mach. Learn. Res. 2023 (2023)- 2016
- [j2]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:
EmoNets: Multimodal deep learning approaches for emotion recognition in video. J. Multimodal User Interfaces 10(2): 99-111 (2016) - 2015
- [j1]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:
Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding. IEEE ACM Trans. Audio Speech Lang. Process. 23(3): 530-539 (2015)
Conference and Workshop Papers
- 2023
- [c34]Atish Agarwala, Yann N. Dauphin:
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. ICML 2023: 152-168 - [c33]Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk:
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. ICML 2023: 17994-18007 - 2022
- [c32]Utku Evci, Yani Ioannou, Cem Keskin, Yann N. Dauphin:
Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win. AAAI 2022: 6577-6586 - [c31]Raphael Gontijo Lopes, Yann N. Dauphin, Ekin Dogus Cubuk:
No One Representation to Rule Them All: Overlapping Features of Training Methods. ICLR 2022 - 2021
- [c30]Yann N. Dauphin, Ekin Dogus Cubuk:
Deconstructing the Regularization of BatchNorm. ICLR 2021 - [c29]Lucio M. Dery, Yann N. Dauphin, David Grangier:
Auxiliary Task Update Decomposition: the Good, the Bad and the neutral. ICLR 2021 - 2020
- [c28]Jiaming Song, Yann N. Dauphin, Michael Auli, Tengyu Ma:
Robust and On-the-Fly Dataset Denoising for Image Classification. ECCV (29) 2020: 556-572 - 2019
- [c27]Angela Fan, Mike Lewis, Yann N. Dauphin:
Strategies for Structuring Story Generation. ACL (1) 2019: 2650-2660 - [c26]Ryan Lowe, Jakob N. Foerster, Y-Lan Boureau, Joelle Pineau, Yann N. Dauphin:
On the Pitfalls of Measuring Emergent Communication. AAMAS 2019: 693-701 - [c25]Kyra Yee, Yann N. Dauphin, Michael Auli:
Simple and Effective Noisy Channel Modeling for Neural Machine Translation. EMNLP/IJCNLP (1) 2019: 5695-5700 - [c24]Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, Michael Auli:
Pay Less Attention with Lightweight and Dynamic Convolutions. ICLR 2019 - [c23]Hongyi Zhang, Yann N. Dauphin, Tengyu Ma:
Fixup Initialization: Residual Learning Without Normalization. ICLR (Poster) 2019 - [c22]Yann N. Dauphin, Samuel S. Schoenholz:
MetaInit: Initializing learning by learning to initialize. NeurIPS 2019: 12624-12636 - 2018
- [c21]Angela Fan, Mike Lewis, Yann N. Dauphin:
Hierarchical Neural Story Generation. ACL (1) 2018: 889-898 - [c20]Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou:
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. ICLR (Workshop) 2018 - [c19]Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz:
mixup: Beyond Empirical Risk Minimization. ICLR (Poster) 2018 - 2017
- [c18]Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin:
A Convolutional Encoder Model for Neural Machine Translation. ACL (1) 2017: 123-135 - [c17]Mike Lewis, Denis Yarats, Yann N. Dauphin, Devi Parikh, Dhruv Batra:
Deal or No Deal? End-to-End Learning of Negotiation Dialogues. EMNLP 2017: 2443-2453 - [c16]Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann N. Dauphin, Nicolas Usunier:
Parseval Networks: Improving Robustness to Adversarial Examples. ICML 2017: 854-863 - [c15]Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier:
Language Modeling with Gated Convolutional Networks. ICML 2017: 933-941 - [c14]Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin:
Convolutional Sequence to Sequence Learning. ICML 2017: 1243-1252 - 2016
- [c13]Yann N. Dauphin, David Grangier:
Predicting distributions with Linearizing Belief Networks. ICLR (Poster) 2016 - 2015
- [c12]Yann N. Dauphin, Harm de Vries, Yoshua Bengio:
Equilibrated adaptive learning rates for non-convex optimization. NIPS 2015: 1504-1512 - 2014
- [c11]Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, KyungHyun Cho, Surya Ganguli, Yoshua Bengio:
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. NIPS 2014: 2933-2941 - [c10]Yann N. Dauphin, Gökhan Tür, Dilek Hakkani-Tür, Larry P. Heck:
Zero-Shot Learning and Clustering for Semantic Utterance Classification. ICLR (Poster) 2014 - 2013
- [c9]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:
Combining modality specific deep neural networks for emotion recognition in video. ICMI 2013: 543-550 - [c8]Yoshua Bengio, Grégoire Mesnil, Yann N. Dauphin, Salah Rifai:
Better Mixing via Deep Representations. ICML (1) 2013: 552-560 - [c7]Yann N. Dauphin, Yoshua Bengio:
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs. NIPS 2013: 1340-1348 - [c6]Yann N. Dauphin, Yoshua Bengio:
Big Neural Networks Waste Capacity. ICLR (Workshop) 2013 - 2012
- [c5]Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio:
A Generative Process for Contractive Auto-Encoders. ICML 2012 - [c4]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:
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. ICML Unsupervised and Transfer Learning 2012: 97-110 - 2011
- [c3]Yann N. Dauphin, Xavier Glorot, Yoshua Bengio:
Large-Scale Learning of Embeddings with Reconstruction Sampling. ICML 2011: 945-952 - [c2]Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller:
The Manifold Tangent Classifier. NIPS 2011: 2294-2302 - [c1]Salah Rifai, Grégoire Mesnil, Pascal Vincent, Xavier Muller, Yoshua Bengio, Yann N. Dauphin, Xavier Glorot:
Higher Order Contractive Auto-Encoder. ECML/PKDD (2) 2011: 645-660
Editorship
- 2021
- [e1]Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan:
Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. 2021 [contents]
Informal and Other Publications
- 2024
- [i35]Yann N. Dauphin, Atish Agarwala, Hossein Mobahi:
Neglected Hessian component explains mysteries in Sharpness regularization. CoRR abs/2401.10809 (2024) - 2023
- [i34]Atish Agarwala, Yann N. Dauphin:
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. CoRR abs/2302.08692 (2023) - [i33]Jonas Ngnawe, Marianne Abemgnigni Njifon, Jonathan Heek, Yann N. Dauphin:
Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets. CoRR abs/2304.02847 (2023) - [i32]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 Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci:
JaxPruner: A concise library for sparsity research. CoRR abs/2304.14082 (2023) - [i31]Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk:
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. CoRR abs/2305.13520 (2023) - [i30]Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan:
Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment. CoRR abs/2306.03262 (2023) - [i29]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. CoRR abs/2311.14115 (2023) - 2022
- [i28]Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, Nihar B. Shah:
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions? CoRR abs/2211.12966 (2022) - 2021
- [i27]Wojciech Sirko, Sergii Kashubin, Marvin Ritter, Abigail Annkah, Yasser Salah Eddine Bouchareb, Yann N. Dauphin, Daniel Keysers, Maxim Neumann, Moustapha Cissé, John Quinn:
Continental-Scale Building Detection from High Resolution Satellite Imagery. CoRR abs/2107.12283 (2021) - [i26]Lucio M. Dery, Yann N. Dauphin, David Grangier:
Auxiliary Task Update Decomposition: The Good, The Bad and The Neutral. CoRR abs/2108.11346 (2021) - [i25]Raphael Gontijo Lopes, Yann N. Dauphin, Ekin D. Cubuk:
No One Representation to Rule Them All: Overlapping Features of Training Methods. CoRR abs/2110.12899 (2021) - 2020
- [i24]Jiaming Song, Lunjia Hu, Yann N. Dauphin, Michael Auli, Tengyu Ma:
Robust and On-the-fly Dataset Denoising for Image Classification. CoRR abs/2003.10647 (2020) - [i23]Utku Evci, Yani Andrew Ioannou, Cem Keskin, Yann N. Dauphin:
Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win. CoRR abs/2010.03533 (2020) - [i22]Atish Agarwala, Jeffrey Pennington, Yann N. Dauphin, Samuel S. Schoenholz:
Temperature check: theory and practice for training models with softmax-cross-entropy losses. CoRR abs/2010.07344 (2020) - 2019
- [i21]Hongyi Zhang, Yann N. Dauphin, Tengyu Ma:
Fixup Initialization: Residual Learning Without Normalization. CoRR abs/1901.09321 (2019) - [i20]Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, Michael Auli:
Pay Less Attention with Lightweight and Dynamic Convolutions. CoRR abs/1901.10430 (2019) - [i19]Angela Fan, Mike Lewis, Yann N. Dauphin:
Strategies for Structuring Story Generation. CoRR abs/1902.01109 (2019) - [i18]Ryan Lowe, Jakob N. Foerster, Y-Lan Boureau, Joelle Pineau, Yann N. Dauphin:
On the Pitfalls of Measuring Emergent Communication. CoRR abs/1903.05168 (2019) - [i17]Kyra Yee, Nathan Ng, Yann N. Dauphin, Michael Auli:
Simple and Effective Noisy Channel Modeling for Neural Machine Translation. CoRR abs/1908.05731 (2019) - [i16]Sara Hooker, Aaron C. Courville, Yann N. Dauphin, Andrea Frome:
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning. CoRR abs/1911.05248 (2019) - 2018
- [i15]Angela Fan, Mike Lewis, Yann N. Dauphin:
Hierarchical Neural Story Generation. CoRR abs/1805.04833 (2018) - 2017
- [i14]Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann N. Dauphin, Nicolas Usunier:
Parseval Networks: Improving Robustness to Adversarial Examples. CoRR abs/1704.08847 (2017) - [i13]Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin:
Convolutional Sequence to Sequence Learning. CoRR abs/1705.03122 (2017) - [i12]Serena Yeung, Anitha Kannan, Yann N. Dauphin, Li Fei-Fei:
Tackling Over-pruning in Variational Autoencoders. CoRR abs/1706.03643 (2017) - [i11]Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou:
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. CoRR abs/1706.04454 (2017) - [i10]Mike Lewis, Denis Yarats, Yann N. Dauphin, Devi Parikh, Dhruv Batra:
Deal or No Deal? End-to-End Learning for Negotiation Dialogues. CoRR abs/1706.05125 (2017) - [i9]Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz:
mixup: Beyond Empirical Risk Minimization. CoRR abs/1710.09412 (2017) - 2016
- [i8]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 abs/1605.02688 (2016) - [i7]Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin:
A Convolutional Encoder Model for Neural Machine Translation. CoRR abs/1611.02344 (2016) - [i6]Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier:
Language Modeling with Gated Convolutional Networks. CoRR abs/1612.08083 (2016) - 2015
- [i5]Yann N. Dauphin, Harm de Vries, Junyoung Chung, Yoshua Bengio:
RMSProp and equilibrated adaptive learning rates for non-convex optimization. CoRR abs/1502.04390 (2015) - [i4]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:
EmoNets: Multimodal deep learning approaches for emotion recognition in video. CoRR abs/1503.01800 (2015) - 2014
- [i3]Razvan Pascanu, Yann N. Dauphin, Surya Ganguli, Yoshua Bengio:
On the saddle point problem for non-convex optimization. CoRR abs/1405.4604 (2014) - [i2]Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio:
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. CoRR abs/1406.2572 (2014) - 2012
- [i1]Yoshua Bengio, Grégoire Mesnil, Yann N. Dauphin, Salah Rifai:
Better Mixing via Deep Representations. CoRR abs/1207.4404 (2012)
Coauthor Index
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