| Adam: A Method for Stochastic Optimization DP Kingma, J Ba Proceedings of the 3rd International Conference on Learning Representations …, 2014 | 109172 | 2014 |
| Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013 | 20793 | 2013 |
| Semi-Supervised Learning with Deep Generative Models DP Kingma, S Mohamed, DJ Rezende, M Welling Advances in Neural Information Processing Systems, 3581-3589, 2014 | 2525 | 2014 |
| Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018 | 1638 | 2018 |
| Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks T Salimans, DP Kingma Advances in Neural Information Processing Systems, 901-901, 2016 | 1471 | 2016 |
| Improved Variational Inference with Inverse Autoregressive Flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling Advances in Neural Information Processing Systems, 4743-4751, 2016 | 1407 | 2016 |
| Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015 | 1080 | 2015 |
| An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019 | 847 | 2019 |
| Adam: A Method for Stochastic Optimization.(2014). arXiv DP Kingma, J Ba arXiv preprint arXiv:1412.6980, 2014 | 802* | 2014 |
| PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications T Salimans, A Karpathy, X Chen, DP Kingma arXiv preprint arXiv:1701.05517, 2017 | 689 | 2017 |
| Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017 | 670 | 2017 |
| Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 582 | 2016 |
| Adam: A method for stochastic optimization preprint DP Kingma, J Ba arXiv preprint arXiv:1412.6980, 2014 | 517* | 2014 |
| Markov Chain Monte Carlo and Variational Inference: Bridging the Gap T Salimans, DP Kingma, M Welling Proceedings of the International Conference on Machine Learning (ICML), 2014 | 516 | 2014 |
| Variational Autoencoders and Nonlinear ICA: A Unifying Framework I Khemakhem, DP Kingma, A Hyvärinen The 23rd International Conference on Artificial Intelligence and Statistics …, 2019 | 195 | 2019 |
| Score-based generative modeling through stochastic differential equations Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole arXiv preprint arXiv:2011.13456, 2020 | 185 | 2020 |
| VideoFlow: A Flow-Based Generative Model for Video M Kumar, M Babaeizadeh, D Erhan, C Finn, S Levine, L Dinh, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2019 | 153* | 2019 |
| GPU Kernels for Block-Sparse Weights S Gray, A Radford, DP Kingma arXiv preprint arXiv:1711.09224 3, 2017 | 124 | 2017 |
| Adam: A method for stochastic optimization in: Proceedings of the 3rd international conference for learning representations (iclr’15) D Kingma, J Ba San Diego, 2015 | 74 | 2015 |
| Adam: A method for stochastic optimization, ILCR DP Kingma, BJ Lei | 71* | 2014 |
Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
