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
341262014
Adam: a method for stochastic optimization (2014). arXiv preprint
D Kingma, J Ba
arXiv preprint arXiv:1412.6980, 0
69
An Introduction to Variational Autoencoders
DP Kingma, M Welling
13*2017
Auto-encoding variational bayes
PK Diederik, M Welling
Proceedings of the International Conference on Learning Representations (ICLR), 2014
272014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
Proceedings of the 2nd International Conference on Learning Representations …, 2013
68942013
Auto-encoding variational bayes (2013)
DP Kingma, M Welling
arXiv preprint arXiv:1312.6114, 2013
602013
Auto-encoding variational bayes. arXiv 2013
DP Kingma, M Welling
arXiv preprint arXiv:1312.6114, 2019
442019
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
392014
Fast Gradient-based Inference With Continuous Latent Variable Models in Auxiliary Form
DP Kingma
arXiv preprint arXiv:1306.0733, 2013
182013
Flow Contrastive Estimation of Energy-Based Model
R Gao, E Nijkamp, Z Xu, AM Dai, DP Kingma, YN Wu
Glow: Generative flow with invertible 1x1 convolutions
DP Kingma, P Dhariwal
Advances in Neural Information Processing Systems, 10215-10224, 2018
2922018
Gpu kernels for block-sparse weights
S Gray, A Radford, DP Kingma
arXiv preprint arXiv:1711.09224, 2017
322017
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
5122016
Improving Score Matching for Learning Statistical Models of Natural Images (M.Sc. Thesis)
DP Kingma
Universiteit Utrecht, 2010
2010
Improving variational autoencoders with inverse autoregressive flow
D Kingma, T Salimans, R Josefowicz, X Chen, I Sutskever, M Welling
7Red Hook, NYCurran Associates, 2017
262017
Improving variational inference with inverse autoregressive flow.(nips), 2016
DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling
URL http://arxiv. org/abs/1606.04934, 0
7
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
arXiv preprint arXiv:1712.01312, 2017
1112017
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
2692014
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
32015
PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture
T Salimans, A Karpathy, X Chen, DP Kingma
ICLR, 0
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