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Variational Diffusion Models


Jul 12, 2021
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho


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How to Train Your Energy-Based Models


Jan 09, 2021
Yang Song, Diederik P. Kingma


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Learning Energy-Based Models by Diffusion Recovery Likelihood


Dec 15, 2020
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma


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Score-Based Generative Modeling through Stochastic Differential Equations


Nov 26, 2020
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole


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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 Hyvärinen


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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 Hyvärinen


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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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