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Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis

Nov 06, 2020
Ron J. Weiss, RJ Skerry-Ryan, Eric Battenberg, Soroosh Mariooryad, Diederik P. Kingma

* 5 pages, 2 figures. submitted to ICASSP 2020 

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On Linear Identifiability of Learned Representations

Jul 08, 2020
Geoffrey Roeder, Luke Metz, Diederik P. Kingma


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ICE-BeeM: Identifiable Conditional Energy-Based Deep Models

Feb 26, 2020
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo HyvÀrinen


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Flow Contrastive Estimation of Energy-Based Models

Dec 02, 2019
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu


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An Introduction to Variational Autoencoders

Jul 24, 2019
Diederik P. Kingma, Max Welling


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Variational Autoencoders and Nonlinear ICA: A Unifying Framework

Jul 10, 2019
Ilyes Khemakhem, Diederik P. Kingma, Aapo HyvÀrinen


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Glow: Generative Flow with Invertible 1x1 Convolutions

Jul 10, 2018
Diederik P. Kingma, Prafulla Dhariwal

* 15 pages; fixed typo in abstract 

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Learning Sparse Neural Networks through $L_0$ Regularization

Jun 22, 2018
Christos Louizos, Max Welling, Diederik P. Kingma

* Published as a conference paper at the International Conference on Learning Representations (ICLR) 2018 

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Variational Lossy Autoencoder

Mar 04, 2017
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

* Added CIFAR10 experiments; ICLR 2017 

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Improving Variational Inference with Inverse Autoregressive Flow

Jan 30, 2017
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling


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Adam: A Method for Stochastic Optimization

Jan 30, 2017
Diederik P. Kingma, Jimmy Ba

* Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015 

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PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Jan 19, 2017
Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma


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Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models

Jun 18, 2016
Jascha Sohl-Dickstein, Diederik P. Kingma


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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

Jun 04, 2016
Tim Salimans, Diederik P. Kingma


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Variational Dropout and the Local Reparameterization Trick

Dec 20, 2015
Diederik P. Kingma, Tim Salimans, Max Welling


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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

May 19, 2015
Tim Salimans, Diederik P. Kingma, Max Welling


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Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets

Jan 22, 2015
Diederik P. Kingma, Max Welling

* Proceedings of The 31st International Conference on Machine Learning, pp. 1782-1790, 2014 

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Semi-Supervised Learning with Deep Generative Models

Oct 31, 2014
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling

* To appear in the proceedings of Neural Information Processing Systems (NIPS) 2014 

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