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Laurent Dinh
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Density estimation using real nvp
L Dinh, J Sohl-Dickstein, S Bengio
arXiv preprint arXiv:1605.08803, 2016
35722016
Nice: Non-linear independent components estimation
L Dinh, D Krueger, Y Bengio
arXiv preprint arXiv:1410.8516, 2014
22302014
Predicting parameters in deep learning
M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas
Advances in neural information processing systems 26, 2013
15842013
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, 2015
14592015
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
9152016
Sharp minima can generalize for deep nets
L Dinh, R Pascanu, S Bengio, Y Bengio
International Conference on Machine Learning, 1019-1028, 2017
7152017
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
2102016
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), 3, 2019
1392019
Techniques for learning binary stochastic feedforward neural networks
T Raiko, M Berglund, G Alain, L Dinh
arXiv preprint arXiv:1406.2989, 2014
1282014
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, 2019
1192019
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
1032019
Gaudi: A neural architect for immersive 3d scene generation
MA Bautista, P Guo, S Abnar, W Talbott, A Toshev, Z Chen, L Dinh, S Zhai, ...
Advances in Neural Information Processing Systems 35, 25102-25116, 2022
942022
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
792020
Density estimation using real NVP (2016)
L Dinh, J Sohl-Dickstein, S Bengio
arXiv preprint arXiv:1605.08803, 2017
742017
Supranormal renographic differential renal function in congenital hydronephrosis: fact, not artifact
G CAPOLICCHIO, R JEDNAK, L DINH, JLP SALLE, A BRZEZINSKI, ...
The Journal of urology 161 (4), 1290-1294, 1999
561999
Learning awareness models
B Amos, L Dinh, S Cabi, T Rothörl, SG Colmenarejo, A Muldal, T Erez, ...
arXiv preprint arXiv:1804.06318, 2018
532018
Theano: A Python framework for fast computation of mathematical expressions. arXiv
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv preprint arXiv:1605.02688 10, 2016
522016
Perfect density models cannot guarantee anomaly detection
C Le Lan, L Dinh
Entropy 23 (12), 1690, 2021
482021
Invertible convolutional flow
M Karami, D Schuurmans, J Sohl-Dickstein, L Dinh, D Duckworth
Advances in Neural Information Processing Systems 32, 2019
472019
A RAD approach to deep mixture models
L Dinh, J Sohl-Dickstein, H Larochelle, R Pascanu
arXiv preprint arXiv:1903.07714, 2019
402019
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