Diederik P. Kingma

According to our database, Diederik P. Kingma authored at least 38 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

DeMo: Decoupled Momentum Optimization.

Bowen Peng, Jeffrey Quesnelle, Diederik P. Kingma

CoRR, 2024

EM Distillation for One-step Diffusion Models.

Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, Ruiqi Gao

CoRR, 2024

2023

Understanding the Diffusion Objective as a Weighted Integral of ELBOs.

Diederik P. Kingma, Ruiqi Gao

CoRR, 2023

Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation.

Diederik P. Kingma, Ruiqi Gao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Distillation of Guided Diffusion Models.

Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

On Distillation of Guided Diffusion Models.

Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans

CoRR, 2022

Imagen Video: High Definition Video Generation with Diffusion Models.

Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey A. Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, Tim Salimans

CoRR, 2022

2021

Variational Diffusion Models.

Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho

CoRR, 2021

How to Train Your Energy-Based Models.

Yang Song, Diederik P. Kingma

CoRR, 2021

On Density Estimation with Diffusion Models.

Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Energy-Based Models by Diffusion Recovery Likelihood.

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

Proceedings of the 9th International Conference on Learning Representations, 2021

Score-Based Generative Modeling through Stochastic Differential Equations.

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

Proceedings of the 9th International Conference on Learning Representations, 2021

Wave-Tacotron: Spectrogram-Free End-to-End Text-to-Speech Synthesis.

Ron J. Weiss, R. J. Skerry-Ryan, Eric Battenberg, Soroosh Mariooryad, Diederik P. Kingma

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

On Linear Identifiability of Learned Representations.

Geoffrey Roeder, Luke Metz, Diederik P. Kingma

CoRR, 2020

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models.

Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen

CoRR, 2020

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA.

Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Flow Contrastive Estimation of Energy-Based Models.

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

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Variational Autoencoders and Nonlinear ICA: A Unifying Framework.

Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

An Introduction to Variational Autoencoders.

Diederik P. Kingma, Max Welling

Found. Trends Mach. Learn., 2019

Variational Autoencoders and Nonlinear ICA: A Unifying Framework.

Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen

CoRR, 2019

2018

Glow: Generative Flow with Invertible 1x1 Convolutions.

Diederik P. Kingma, Prafulla Dhariwal

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Sparse Neural Networks through L_0 Regularization.

Christos Louizos, Max Welling, Diederik P. Kingma

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Learning Sparse Neural Networks through L₀ Regularization.

Christos Louizos, Max Welling, Diederik P. Kingma

CoRR, 2017

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

Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma

Proceedings of the 5th International Conference on Learning Representations, 2017

Variational Lossy Autoencoder.

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

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Improving Variational Inference with Inverse Autoregressive Flow.

Diederik P. Kingma, Tim Salimans, Max Welling

CoRR, 2016

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

Tim Salimans, Diederik P. Kingma

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Improving Variational Autoencoders with Inverse Autoregressive Flow.

Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

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

Jascha Sohl-Dickstein, Diederik P. Kingma

CoRR, 2015

Adam: A Method for Stochastic Optimization.

Diederik P. Kingma, Jimmy Ba

Proceedings of the 3rd International Conference on Learning Representations, 2015

Variational Recurrent Auto-Encoders.

Otto Fabius, Joost R. van Amersfoort, Diederik P. Kingma

Proceedings of the 3rd International Conference on Learning Representations, 2015

Variational Dropout and the Local Reparameterization Trick.

Diederik P. Kingma, Tim Salimans, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap.

Tim Salimans, Diederik P. Kingma, Max Welling

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Auto-Encoding Variational Bayes.

Diederik P. Kingma, Max Welling

Proceedings of the 2nd International Conference on Learning Representations, 2014

Semi-supervised Learning with Deep Generative Models.

Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

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

Diederik P. Kingma, Max Welling

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form.

Diederik P. Kingma

CoRR, 2013

2010

Regularized estimation of image statistics by Score Matching.

Diederik P. Kingma, Yann LeCun

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010