| Variational autoencoders and nonlinear ica: A unifying framework I Khemakhem, DP Kingma, A Hyvärinen arXiv preprint arXiv:1907.04809, 2019 | 4 | 2019 |
| VideoFlow: A Flow-Based Generative Model for Video M Kumar, M Babaeizadeh, D Erhan, C Finn, S Levine, L Dinh, DP Kingma arXiv preprint arXiv:1903.01434, 2019 | 19 | 2019 |
| Auto-encoding variational bayes. arXiv 2013 DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2019 | 44 | 2019 |
| Glow: Generative flow with invertible 1x1 convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018 | 292 | 2018 |
| Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma arXiv preprint arXiv:1712.01312, 2017 | 111 | 2017 |
| Variational Inference and Deep Learning: A New Synthesis (Ph.D. Thesis) DP Kingma Universiteit van Amsterdam, 2017 | 15* | 2017 |
| 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 | 237 | 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 | 26 | 2017 |
| An Introduction to Variational Autoencoders DP Kingma, M Welling | 13* | 2017 |
| Gpu kernels for block-sparse weights S Gray, A Radford, DP Kingma arXiv preprint arXiv:1711.09224, 2017 | 32 | 2017 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 257 | 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 | 512 | 2016 |
| 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 | 598 | 2016 |
| Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models J Sohl-Dickstein, DP Kingma arXiv preprint arXiv:1504.08025, 2015 | 3 | 2015 |
| Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015 | 407 | 2015 |
| Adam: A Method for Stochastic Optimization DP Kingma, J Ba Proceedings of the 3rd International Conference on Learning Representations …, 2014 | 34126 | 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 | 269 | 2014 |
| Stochastic gradient VB and the variational auto-encoder DP Kingma, M Welling Second International Conference on Learning Representations, ICLR, 2014 | 112 | 2014 |
| Auto-encoding variational bayes PK Diederik, M Welling Proceedings of the International Conference on Learning Representations (ICLR), 2014 | 27 | 2014 |
| Semi-Supervised Learning with Deep Generative Models DP Kingma, S Mohamed, DJ Rezende, M Welling Advances in Neural Information Processing Systems, 3581-3589, 2014 | 1202 | 2014 |
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
