| Adam: A Method for Stochastic Optimization DP Kingma, J Ba Proceedings of the 3rd International Conference on Learning Representations …, 2014 | 49840 | 2014 |
| Auto-Encoding Variational Bayes DP Kingma, M Welling Proceedings of the 2nd International Conference on Learning Representations …, 2013 | 9907 | 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 | 1549 | 2014 |
| 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 | 805 | 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 | 786 | 2016 |
| Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018 | 566 | 2018 |
| Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015 | 543 | 2015 |
| Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 349 | 2016 |
| 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 | 345 | 2017 |
| 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 | 339 | 2014 |
| Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017 | 247 | 2017 |
| A Method for Stochastic Optimization. arXiv e-prints, page DP Kingma, JB Adam arXiv preprint arXiv:1412.6980 4, 2014 | 63 | 2014 |
| An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019 | 60 | 2019 |
| 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 | 51 | 2019 |
| Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets DP Kingma, M Welling Proceedings of the International Conference on Machine Learning (ICML), 2014 | 51 | 2014 |
| GPU Kernels for Block-Sparse Weights S Gray, A Radford, DP Kingma arXiv preprint arXiv:1711.09224 3, 2017 | 50 | 2017 |
| Regularized Estimation of Image Statistics by Score Matching DP Kingma, Y LeCun Advances in Neural Information Processing Systems 23, 1126-1134, 2010 | 47 | 2010 |
| Variational Autoencoders and Nonlinear ICA: A Unifying Framework I Khemakhem, DP Kingma, A Hyvärinen The 23rd International Conference on Artificial Intelligence and Statistics …, 2019 | 25 | 2019 |
| Variational Inference and Deep Learning: A New Synthesis (Ph.D. Thesis) DP Kingma Universiteit van Amsterdam, 2017 | 25* | 2017 |
| Fast Gradient-based Inference With Continuous Latent Variable Models in Auxiliary Form DP Kingma arXiv preprint arXiv:1306.0733, 2013 | 23 | 2013 |
Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
