| Density estimation using real nvp L Dinh, J Sohl-Dickstein, S Bengio arXiv preprint arXiv:1605.08803, 2016 | 2513 | 2016 |
| Nice: Non-linear independent components estimation L Dinh, D Krueger, Y Bengio arXiv preprint arXiv:1410.8516, 2014 | 1597 | 2014 |
| Predicting parameters in deep learning M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas Advances in neural information processing systems 26, 2013 | 1367 | 2013 |
| 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 | 1182 | 2015 |
| 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 | 871 | 2016 |
| Sharp minima can generalize for deep nets L Dinh, R Pascanu, S Bengio, Y Bengio International Conference on Machine Learning, 1019-1028, 2017 | 567 | 2017 |
| 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 | 189 | 2016 |
| Techniques for learning binary stochastic feedforward neural networks T Raiko, M Berglund, G Alain, L Dinh arXiv preprint arXiv:1406.2989, 2014 | 123 | 2014 |
| 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 | 113 | 2019 |
| 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 | 89 | 2019 |
| 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 | 77 | 2019 |
| 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 | 54 | 2020 |
| Density estimation using real NVP (2016) L Dinh, J Sohl-Dickstein, S Bengio arXiv preprint arXiv:1605.08803, 2017 | 53 | 2017 |
| 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 | 53 | 1999 |
| Learning awareness models B Amos, L Dinh, S Cabi, T Rothörl, SG Colmenarejo, A Muldal, T Erez, ... arXiv preprint arXiv:1804.06318, 2018 | 43 | 2018 |
| Invertible convolutional flow M Karami, D Schuurmans, J Sohl-Dickstein, L Dinh, D Duckworth Advances in Neural Information Processing Systems 32, 2019 | 36 | 2019 |
| A RAD approach to deep mixture models L Dinh, J Sohl-Dickstein, H Larochelle, R Pascanu arXiv preprint arXiv:1903.07714, 2019 | 35 | 2019 |
| 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, 2016 | 34 | 2016 |
| Perfect density models cannot guarantee anomaly detection C Le Lan, L Dinh Entropy 23 (12), 1690, 2021 | 33 | 2021 |
| 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 | 33 | 2016 |
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
