Yann N. Dauphin
Yann N. Dauphin
Google AI
Verified email at - Homepage
Cited by
Cited by
mixup: Beyond empirical risk minimization
H Zhang, M Cisse, YN Dauphin, D Lopez-Paz
arXiv preprint arXiv:1710.09412, 2017
Convolutional sequence to sequence learning
J Gehring, M Auli, D Grangier, D Yarats, YN Dauphin
International conference on machine learning, 1243-1252, 2017
Language modeling with gated convolutional networks
YN Dauphin, A Fan, M Auli, D Grangier
International conference on machine learning, 933-941, 2017
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YN Dauphin, R Pascanu, C Gulcehre, K Cho, S Ganguli, Y Bengio
Advances in neural information processing systems 27, 2014
Hierarchical neural story generation
A Fan, M Lewis, Y Dauphin
arXiv preprint arXiv:1805.04833, 2018
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv e-prints, arXiv: 1605.02688, 2016
Parseval networks: Improving robustness to adversarial examples
M Cisse, P Bojanowski, E Grave, Y Dauphin, N Usunier
International conference on machine learning, 854-863, 2017
Using recurrent neural networks for slot filling in spoken language understanding
G Mesnil, Y Dauphin, K Yao, Y Bengio, L Deng, D Hakkani-Tur, X He, ...
IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (3), 530-539, 2014
Equilibrated adaptive learning rates for non-convex optimization
Y Dauphin, H De Vries, Y Bengio
Advances in neural information processing systems 28, 2015
Pay less attention with lightweight and dynamic convolutions
F Wu, A Fan, A Baevski, YN Dauphin, M Auli
arXiv preprint arXiv:1901.10430, 2019
A convolutional encoder model for neural machine translation
J Gehring, M Auli, D Grangier, YN Dauphin
arXiv preprint arXiv:1611.02344, 2016
Emonets: Multimodal deep learning approaches for emotion recognition in video
SE Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, K Konda, ...
Journal on Multimodal User Interfaces 10, 99-111, 2016
Deal or no deal? end-to-end learning for negotiation dialogues
M Lewis, D Yarats, YN Dauphin, D Parikh, D Batra
arXiv preprint arXiv:1706.05125, 2017
Better mixing via deep representations
Y Bengio, G Mesnil, Y Dauphin, S Rifai
International conference on machine learning, 552-560, 2013
Combining modality specific deep neural networks for emotion recognition in video
SE Kahou, C Pal, X Bouthillier, P Froumenty, Ç Gülçehre, R Memisevic, ...
Proceedings of the 15th ACM on International conference on multimodal …, 2013
Empirical analysis of the hessian of over-parametrized neural networks
L Sagun, U Evci, VU Guney, Y Dauphin, L Bottou
arXiv preprint arXiv:1706.04454, 2017
Fixup initialization: Residual learning without normalization
H Zhang, YN Dauphin, T Ma
arXiv preprint arXiv:1901.09321, 2019
The manifold tangent classifier
S Rifai, YN Dauphin, P Vincent, Y Bengio, X Muller
Advances in neural information processing systems 24, 2011
Unsupervised and transfer learning challenge: a deep learning approach
G Mesnil, Y Dauphin, X Glorot, S Rifai, Y Bengio, I Goodfellow, E Lavoie, ...
Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 97-110, 2012
Lopez-Paz, D. mixup: Beyond empirical risk minimization. arXiv 2017
H Zhang, M Cisse, YN Dauphin
arXiv preprint arXiv:1710.09412, 2019
The system can't perform the operation now. Try again later.
Articles 1–20