Diederik P. Kingma

52 papers A* 12A 1Misc 1Journal 27Unranked 11

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Adam-mini: Use Fewer Learning Rates To Gain More. ICLR Yushun Zhang, Congliang Chen, Ziniu Li, Tian Ding, Chenwei Wu, Diederik P. Kingma, Yinyu Ye, Zhi-Quan Luo, Ruoyu Sun
2024	J	`jnl`	DeMo: Decoupled Momentum Optimization. CoRR Bowen Peng, Jeffrey Quesnelle, Diederik P. Kingma
2024	A*	`conf`	EM Distillation for One-step Diffusion Models. NeurIPS Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin P. Murphy, Tim Salimans, Ben Poole, Ruiqi Gao
2024	J	`jnl`	EM Distillation for One-step Diffusion Models. CoRR Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, Ruiqi Gao
2023	A*	`conf`	On Distillation of Guided Diffusion Models. CVPR Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans
2023	A*	`conf`	Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation. NeurIPS Diederik P. Kingma, Ruiqi Gao
2023	J	`jnl`	Understanding the Diffusion Objective as a Weighted Integral of ELBOs. CoRR Diederik P. Kingma, Ruiqi Gao
2022	J	`jnl`	Imagen Video: High Definition Video Generation with Diffusion Models. CoRR 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
2022	J	`jnl`	On Distillation of Guided Diffusion Models. CoRR Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans
2021	J	`jnl`	How to Train Your Energy-Based Models. CoRR Yang Song, Diederik P. Kingma
2021	A*	`conf`	Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
2021	A*	`conf`	On Density Estimation with Diffusion Models. NeurIPS Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
2021	A*	`conf`	Score-Based Generative Modeling through Stochastic Differential Equations. ICLR Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
2021	J	`jnl`	Variational Diffusion Models. CoRR Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
2021	Misc	`conf`	Wave-Tacotron: Spectrogram-Free End-to-End Text-to-Speech Synthesis. ICASSP Ron J. Weiss, R. J. Skerry-Ryan, Eric Battenberg, Soroosh Mariooryad, Diederik P. Kingma
2020	A*	`conf`	Flow Contrastive Estimation of Energy-Based Models. CVPR Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
2020	A*	`conf`	ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. NeurIPS Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
2020	J	`jnl`	ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. CoRR Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
2020	J	`jnl`	Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
2020	J	`jnl`	On Linear Identifiability of Learned Representations. CoRR Geoffrey Roeder, Luke Metz, Diederik P. Kingma
2020	J	`jnl`	Score-Based Generative Modeling through Stochastic Differential Equations. CoRR Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
2020	A	`conf`	Variational Autoencoders and Nonlinear ICA: A Unifying Framework. AISTATS Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen
2020	J	`jnl`	Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis. CoRR Ron J. Weiss, R. J. Skerry-Ryan, Eric Battenberg, Soroosh Mariooryad, Diederik P. Kingma
2019	J	`jnl`	An Introduction to Variational Autoencoders. CoRR Diederik P. Kingma, Max Welling
2019	J	`jnl`	An Introduction to Variational Autoencoders. Found. Trends Mach. Learn. Diederik P. Kingma, Max Welling
2019	J	`jnl`	Flow Contrastive Estimation of Energy-Based Models. CoRR Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
2019	J	`jnl`	Variational Autoencoders and Nonlinear ICA: A Unifying Framework. CoRR Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen
2018	A*	`conf`	Glow: Generative Flow with Invertible 1x1 Convolutions. NeurIPS Diederik P. Kingma, Prafulla Dhariwal
2018	J	`jnl`	Glow: Generative Flow with Invertible 1x1 Convolutions. CoRR Diederik P. Kingma, Prafulla Dhariwal
2018	—	`conf`	Learning Sparse Neural Networks through L_0 Regularization. ICLR (Poster) Christos Louizos, Max Welling, Diederik P. Kingma
2017	J	`jnl`	Learning Sparse Neural Networks through L CoRR Christos Louizos, Max Welling, Diederik P. Kingma
2017	—	`conf`	PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. ICLR (Poster) Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
2017	J	`jnl`	PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. CoRR Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
2017	—	`conf`	Variational Lossy Autoencoder. ICLR (Poster) Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	—	`conf`	Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling
2016	J	`jnl`	Improving Variational Inference with Inverse Autoregressive Flow. CoRR Diederik P. Kingma, Tim Salimans, Max Welling
2016	J	`jnl`	Variational Lossy Autoencoder. CoRR Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	—	`conf`	Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. NIPS Tim Salimans, Diederik P. Kingma
2016	J	`jnl`	Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. CoRR Tim Salimans, Diederik P. Kingma
2015	—	`conf`	Adam: A Method for Stochastic Optimization. ICLR (Poster) Diederik P. Kingma, Jimmy Ba
2015	A*	`conf`	Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. ICML Tim Salimans, Diederik P. Kingma, Max Welling
2015	J	`jnl`	Technical Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models. CoRR Jascha Sohl-Dickstein, Diederik P. Kingma
2015	—	`conf`	Variational Dropout and the Local Reparameterization Trick. NIPS Diederik P. Kingma, Tim Salimans, Max Welling
2015	J	`jnl`	Variational Dropout and the Local Reparameterization Trick. CoRR Diederik P. Kingma, Tim Salimans, Max Welling
2015	—	`conf`	Variational Recurrent Auto-Encoders. ICLR (Workshop) Otto Fabius, Joost R. van Amersfoort, Diederik P. Kingma
2014	—	`conf`	Auto-Encoding Variational Bayes. ICLR Diederik P. Kingma, Max Welling
2014	A*	`conf`	Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. ICML Diederik P. Kingma, Max Welling
2014	J	`jnl`	Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. CoRR Diederik P. Kingma, Max Welling
2014	J	`jnl`	Semi-Supervised Learning with Deep Generative Models. CoRR Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling
2014	—	`conf`	Semi-supervised Learning with Deep Generative Models. NIPS Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
2013	J	`jnl`	Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form. CoRR Diederik P. Kingma
2010	—	`conf`	Regularized estimation of image statistics by Score Matching. NIPS Diederik P. Kingma, Yann LeCun