Laurent Dinh
Title
Cited by
Cited by
Year
Density estimation using real nvp
L Dinh, J Sohl-Dickstein, S Bengio
arXiv preprint arXiv:1605.08803, 2016
14622016
Predicting parameters in deep learning
M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas
arXiv preprint arXiv:1306.0543, 2013
10982013
Nice: Non-linear independent components estimation
L Dinh, D Krueger, Y Bengio
arXiv preprint arXiv:1410.8516, 2014
9772014
A recurrent latent variable model for sequential data
J Chung, K Kastner, L Dinh, K Goel, AC Courville, Y Bengio
Advances in neural information processing systems 28, 2980-2988, 2015
8682015
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
6692016
Sharp minima can generalize for deep nets
L Dinh, R Pascanu, S Bengio, Y Bengio
International Conference on Machine Learning, 1019-1028, 2017
3772017
Theano: A Python framework for fast computation of mathematical expressions
TTD Team, R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, ...
arXiv preprint arXiv:1605.02688, 2016
1722016
Techniques for learning binary stochastic feedforward neural networks
T Raiko, M Berglund, G Alain, L Dinh
arXiv preprint arXiv:1406.2989, 2014
1102014
Videoflow: A flow-based generative model for video
M Kumar, M Babaeizadeh, D Erhan, C Finn, S Levine, L Dinh, D Kingma
arXiv preprint arXiv:1903.01434 2 (5), 2019
802019
Discrete flows: Invertible generative models of discrete data
D Tran, K Vafa, K Agrawal, L Dinh, B Poole
Advances in Neural Information Processing Systems 32, 14719-14728, 2019
572019
Fast approximate natural gradient descent in a kronecker-factored eigenbasis
T George, C Laurent, X Bouthillier, N Ballas, P Vincent
arXiv preprint arXiv:1806.03884, 2018
572018
Videoflow: A conditional flow-based model for stochastic video generation
M Kumar, M Babaeizadeh, D Erhan, C Finn, S Levine, L Dinh, D Kingma
arXiv preprint arXiv:1903.01434, 2019
402019
Unreproducible research is reproducible
X Bouthillier, C Laurent, P Vincent
International Conference on Machine Learning, 725-734, 2019
342019
Learning awareness models
B Amos, L Dinh, S Cabi, T Rothörl, SG Colmenarejo, A Muldal, T Erez, ...
arXiv preprint arXiv:1804.06318, 2018
322018
Harm de Vries, David Warde-Farley, Dustin J
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, and …, 2016
272016
Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
CW Huang, L Dinh, A Courville
arXiv preprint arXiv:2002.07101, 2020
262020
Deep independence network analysis of structural brain imaging: application to schizophrenia
E Castro, RD Hjelm, SM Plis, L Dinh, JA Turner, VD Calhoun
IEEE transactions on medical imaging 35 (7), 1729-1740, 2016
242016
A RAD approach to deep mixture models
L Dinh, J Sohl-Dickstein, H Larochelle, R Pascanu
arXiv preprint arXiv:1903.07714, 2019
222019
Learnable explicit density for continuous latent space and variational inference
CW Huang, A Touati, L Dinh, M Drozdzal, M Havaei, L Charlin, ...
arXiv preprint arXiv:1710.02248, 2017
212017
Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
URL http://arxiv. org/abs/1605.02688, 2016
212016
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