Diederik P. Kingma

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 41

Top Topics

Diffusion Models

Variational Bayes

Top Venues

Publications

Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, Ruiqi Gao
EM Distillation for One-step Diffusion Models. CoRR (2024)
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans
On Distillation of Guided Diffusion Models. CVPR (2023)
Diederik P. Kingma, Ruiqi Gao
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation. NeurIPS (2023)
Diederik P. Kingma, Ruiqi Gao
Understanding the Diffusion Objective as a Weighted Integral of ELBOs. CoRR (2023)
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
Imagen Video: High Definition Video Generation with Diffusion Models. CoRR (2022)
Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans
On Distillation of Guided Diffusion Models. CoRR (2022)
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
Variational Diffusion Models. CoRR (2021)
Ron J. Weiss, R. J. Skerry-Ryan, Eric Battenberg, Soroosh Mariooryad, Diederik P. Kingma
Wave-Tacotron: Spectrogram-Free End-to-End Text-to-Speech Synthesis. ICASSP (2021)
Yang Song, Diederik P. Kingma
How to Train Your Energy-Based Models. CoRR (2021)
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR (2021)
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
On Density Estimation with Diffusion Models. NeurIPS (2021)
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Score-Based Generative Modeling through Stochastic Differential Equations. ICLR (2021)
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Score-Based Generative Modeling through Stochastic Differential Equations. CoRR (2020)
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
Flow Contrastive Estimation of Energy-Based Models. CVPR (2020)
Ron J. Weiss, R. J. Skerry-Ryan, Eric Battenberg, Soroosh Mariooryad, Diederik P. Kingma
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis. CoRR (2020)
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR (2020)
Geoffrey Roeder, Luke Metz, Diederik P. Kingma
On Linear Identifiability of Learned Representations. CoRR (2020)
Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. AISTATS (2020)
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. NeurIPS (2020)
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. CoRR (2020)
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
Flow Contrastive Estimation of Energy-Based Models. CoRR (2019)
Diederik P. Kingma, Max Welling
An Introduction to Variational Autoencoders. Found. Trends Mach. Learn. 12 (4) (2019)
Diederik P. Kingma, Max Welling
An Introduction to Variational Autoencoders. CoRR (2019)
Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. CoRR (2019)
Christos Louizos, Max Welling, Diederik P. Kingma
Learning Sparse Neural Networks through L_0 Regularization. ICLR (Poster) (2018)
Diederik P. Kingma, Prafulla Dhariwal
Glow: Generative Flow with Invertible 1x1 Convolutions. NeurIPS (2018)
Diederik P. Kingma, Prafulla Dhariwal
Glow: Generative Flow with Invertible 1x1 Convolutions. CoRR (2018)
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Variational Lossy Autoencoder. ICLR (Poster) (2017)
Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. CoRR (2017)
Christos Louizos, Max Welling, Diederik P. Kingma
Regularization. CoRR (2017)
Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. ICLR (Poster) (2017)
Diederik P. Kingma, Tim Salimans, Max Welling
Improving Variational Inference with Inverse Autoregressive Flow. CoRR (2016)
Tim Salimans, Diederik P. Kingma
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. NIPS (2016)
Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling
Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS (2016)
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Variational Lossy Autoencoder. CoRR (2016)
Tim Salimans, Diederik P. Kingma
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. CoRR (2016)
Diederik P. Kingma, Jimmy Ba
Adam: A Method for Stochastic Optimization. ICLR (Poster) (2015)
Jascha Sohl-Dickstein, Diederik P. Kingma
Technical Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models. CoRR (2015)
Otto Fabius, Joost R. van Amersfoort, Diederik P. Kingma
Variational Recurrent Auto-Encoders. ICLR (Workshop) (2015)
Tim Salimans, Diederik P. Kingma, Max Welling
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. ICML (2015)
Diederik P. Kingma, Tim Salimans, Max Welling
Variational Dropout and the Local Reparameterization Trick. CoRR (2015)
Diederik P. Kingma, Max Welling
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. CoRR (2014)
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
Semi-supervised Learning with Deep Generative Models. NIPS (2014)
Diederik P. Kingma, Max Welling
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. ICML (2014)
Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling
Semi-Supervised Learning with Deep Generative Models. CoRR (2014)
Diederik P. Kingma, Max Welling
Auto-Encoding Variational Bayes. ICLR (2014)
Diederik P. Kingma
Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form. CoRR (2013)
Diederik P. Kingma, Yann LeCun
Regularized estimation of image statistics by Score Matching. NIPS (2010)