Regularized estimation of image statistics by Score Matching. - Diederik P. Kingma; Yann LeCun (NIPS, 2010)

Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form. - Diederik P. Kingma (CoRR, 2013)

Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. - Diederik P. Kingma; Max Welling (ICML, 2014)

Semi-supervised Learning with Deep Generative Models. - Diederik P. Kingma; Shakir Mohamed; Danilo Jimenez Rezende; Max Welling (NIPS, 2014)

Auto-Encoding Variational Bayes. - Diederik P. Kingma; Max Welling (ICLR, 2014)

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. - Tim Salimans; Diederik P. Kingma; Max Welling (ICML, 2015)

Variational Dropout and the Local Reparameterization Trick. - Diederik P. Kingma; Tim Salimans; Max Welling (NIPS, 2015)

Variational Recurrent Auto-Encoders. - Otto Fabius; Joost R. van Amersfoort; Diederik P. Kingma (ICLR (Workshop), 2015)

Adam: A Method for Stochastic Optimization. - Diederik P. Kingma; Jimmy Ba (ICLR (Poster), 2015)

Technical Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models. - Jascha Sohl-Dickstein; Diederik P. Kingma (CoRR, 2015)

Improving Variational Autoencoders with Inverse Autoregressive Flow. - Diederik P. Kingma; Tim Salimans; Rafal Józefowicz; Xi Chen 022; Ilya Sutskever; Max Welling (NIPS, 2016)

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. - Tim Salimans; Diederik P. Kingma (NIPS, 2016)

Improving Variational Inference with Inverse Autoregressive Flow. - Diederik P. Kingma; Tim Salimans; Max Welling (CoRR, 2016)

Variational Lossy Autoencoder. - Xi Chen 022; Diederik P. Kingma; Tim Salimans; Yan Duan; Prafulla Dhariwal; John Schulman; Ilya Sutskever; Pieter Abbeel (ICLR (Poster), 2017)

PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. - Tim Salimans; Andrej Karpathy; Xi Chen 022; Diederik P. Kingma (ICLR (Poster), 2017)

Learning Sparse Neural Networks through L0 Regularization. - Christos Louizos; Max Welling; Diederik P. Kingma (CoRR, 2017)

Learning Sparse Neural Networks through L_0 Regularization. - Christos Louizos; Max Welling; Diederik P. Kingma (ICLR (Poster), 2018)

Glow: Generative Flow with Invertible 1x1 Convolutions. - Diederik P. Kingma; Prafulla Dhariwal (NeurIPS, 2018)

Variational Autoencoders and Nonlinear ICA: A Unifying Framework. - Ilyes Khemakhem; Diederik P. Kingma; Aapo Hyvärinen (CoRR, 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; Ricardo Pio Monti; Aapo Hyvärinen (AISTATS, 2020)

Flow Contrastive Estimation of Energy-Based Models. - Ruiqi Gao; Erik Nijkamp; Diederik P. Kingma; Zhen Xu 006; Andrew M. Dai; Ying Nian Wu (CVPR, 2020)

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. - Ilyes Khemakhem; Ricardo Pio Monti; Diederik P. Kingma; Aapo Hyvärinen (NeurIPS, 2020)

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. - Ilyes Khemakhem; Ricardo Pio Monti; Diederik P. Kingma; Aapo Hyvärinen (CoRR, 2020)

On Linear Identifiability of Learned Representations. - Geoffrey Roeder; Luke Metz; Diederik P. Kingma (CoRR, 2020)

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 (ICASSP, 2021)

Score-Based Generative Modeling through Stochastic Differential Equations. - Yang Song 011; Jascha Sohl-Dickstein; Diederik P. Kingma; Abhishek Kumar; Stefano Ermon; Ben Poole (ICLR, 2021)

Learning Energy-Based Models by Diffusion Recovery Likelihood. - Ruiqi Gao; Yang Song 011; Ben Poole; Ying Nian Wu; Diederik P. Kingma (ICLR, 2021)

On Density Estimation with Diffusion Models. - Diederik P. Kingma; Tim Salimans; Ben Poole; Jonathan Ho (NeurIPS, 2021)

How to Train Your Energy-Based Models. - Yang Song 011; Diederik P. Kingma (CoRR, 2021)

Variational Diffusion Models. - Diederik P. Kingma; Tim Salimans; Ben Poole; Jonathan Ho (CoRR, 2021)

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 002; David J. Fleet; Tim Salimans (CoRR, 2022)

On Distillation of Guided Diffusion Models. - Chenlin Meng; Ruiqi Gao; Diederik P. Kingma; Stefano Ermon; Jonathan Ho; Tim Salimans (CoRR, 2022)

On Distillation of Guided Diffusion Models. - Chenlin Meng; Robin Rombach; Ruiqi Gao; Diederik P. Kingma; Stefano Ermon; Jonathan Ho; Tim Salimans (CVPR, 2023)

Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation. - Diederik P. Kingma; Ruiqi Gao (NeurIPS, 2023)

Understanding the Diffusion Objective as a Weighted Integral of ELBOs. - Diederik P. Kingma; Ruiqi Gao (CoRR, 2023)

EM Distillation for One-step Diffusion Models. - Sirui Xie; Zhisheng Xiao; Diederik P. Kingma; Tingbo Hou; Ying Nian Wu; Kevin P. Murphy; Tim Salimans; Ben Poole; Ruiqi Gao (NeurIPS, 2024)

DeMo: Decoupled Momentum Optimization. - Bowen Peng; Jeffrey Quesnelle; Diederik P. Kingma (CoRR, 2024)

Adam-mini: Use Fewer Learning Rates To Gain More. - Yushun Zhang; Congliang Chen; Ziniu Li; Tian Ding; Chenwei Wu 002; Diederik P. Kingma; Yinyu Ye 001; Zhi-Quan Luo; Ruoyu Sun 001 (ICLR, 2025)