| Adam: A Method for Stochastic Optimization DP Kingma, J Ba Proceedings of the 3rd International Conference on Learning Representations …, 2014 | 32447 | 2014 |
| Auto-Encoding Variational Bayes DP Kingma, M Welling Proceedings of the 2nd International Conference on Learning Representations …, 2013 | 6648 | 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 | 1164 | 2014 |
| A method for stochastic optimization D Kingma, J Ba | 565 | 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 | 561 | 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 | 496 | 2016 |
| Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015 | 397 | 2015 |
| 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 | 268 | 2014 |
| Glow: Generative flow with invertible 1x1 convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018 | 264 | 2018 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 244 | 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 | 228 | 2017 |
| Stochastic gradient VB and the variational auto-encoder DP Kingma, M Welling Second International Conference on Learning Representations, ICLR, 2014 | 106 | 2014 |
| Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma arXiv preprint arXiv:1712.01312, 2017 | 102 | 2017 |
| Auto-encoding variational bayes (2013) DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013 | 56 | 2013 |
| Auto-encoding variational bayes. arXiv 2013 DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2019 | 43 | 2019 |
| Regularized Estimation of Image Statistics by Score Matching DP Kingma, Y LeCun Advances in Neural Information Processing Systems 23, 1126-1134, 2010 | 42 | 2010 |
| 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 | 38 | 2014 |
| Gpu kernels for block-sparse weights S Gray, A Radford, DP Kingma arXiv preprint arXiv:1711.09224, 2017 | 30 | 2017 |
| Improving variational autoencoders with inverse autoregressive flow D Kingma, T Salimans, R Josefowicz, X Chen, I Sutskever, M Welling 7Red Hook, NYCurran Associates, 2017 | 25 | 2017 |
| Auto-encoding variational bayes PK Diederik, M Welling Proceedings of the International Conference on Learning Representations (ICLR), 2014 | 24 | 2014 |
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
