Diederik P. Kingma
Diederik P. Kingma
Research Scientist, Google Brain
Verified email at google.com - Homepage
TitleCited byYear
Adam: A Method for Stochastic Optimization
DP Kingma, J Ba
Proceedings of the 3rd International Conference on Learning Representations …, 2014
385952014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
Proceedings of the 2nd International Conference on Learning Representations …, 2013
77652013
Semi-Supervised Learning with Deep Generative Models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in Neural Information Processing Systems, 3581-3589, 2014
13112014
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
6692016
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
6002016
Variational Dropout and the Local Reparameterization Trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
4492015
Glow: Generative flow with invertible 1x1 convolutions
DP Kingma, P Dhariwal
Advances in Neural Information Processing Systems, 10215-10224, 2018
3482018
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
2882014
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
2762016
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
2632017
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
arXiv preprint arXiv:1712.01312, 2017
1502017
Stochastic gradient VB and the variational auto-encoder
DP Kingma, M Welling
Second International Conference on Learning Representations, ICLR, 2014
1292014
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
452014
Regularized Estimation of Image Statistics by Score Matching
DP Kingma, Y LeCun
Advances in Neural Information Processing Systems 23, 1126-1134, 2010
442010
Gpu kernels for block-sparse weights
S Gray, A Radford, DP Kingma
arXiv preprint arXiv:1711.09224 3, 2017
362017
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
272019
Auto-encoding variational bayes
PK Diederik, M Welling
Proceedings of the International Conference on Learning Representations (ICLR), 2014
232014
Adam: a method for stochastic optimization (2014). arXiv preprint
D Kingma, J Ba
arXiv preprint arXiv:1412.6980, 0
22
Fast Gradient-based Inference With Continuous Latent Variable Models in Auxiliary Form
DP Kingma
arXiv preprint arXiv:1306.0733, 2013
192013
Variational Inference and Deep Learning: A New Synthesis (Ph.D. Thesis)
DP Kingma
Universiteit van Amsterdam, 2017
16*2017
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Articles 1–20