Diederik P. Kingma

Adam: A Method for Stochastic Optimization
Diederik P. Kingma, Jimmy Lei Ba
2014arXiv (Cornell University)99K citations Open access
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Auto-Encoding Variational Bayes
Diederik P. Kingma, Max Welling
2013UvA-DARE (University of Amsterdam)18K citations Open access
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An Introduction to Variational Autoencoders
Diederik P. Kingma, Max Welling
2019Foundations and Trends® in Machine Learning2,597 citations Open access
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Score-Based Generative Modeling through Stochastic Differential Equations
Yang Sup Song, Jascha Sohl‐Dickstein, Diederik P. Kingma, Abhishek Kumar +2 more
2020arXiv (Cornell University)1,951 citations Open access
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Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling
2014UvA-DARE (University of Amsterdam)1,646 citations Open access
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Tim Salimans, Diederik P. Kingma
2016arXiv (Cornell University)1,092 citations Open access
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Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma, Prafulla Dhariwal
2018arXiv (Cornell University)825 citations Open access
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Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma, Tim Salimans, Max Welling
2015arXiv (Cornell University)708 citations Open access
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An Introduction to Variational Autoencoders
Diederik P. Kingma, Max Welling
2019645 citations Open access
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Learning Sparse Neural Networks through $L_0$ Regularization
Christos Louizos, Max Welling, Diederik P. Kingma
2017arXiv (Cornell University)629 citations Open access
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Weight normalization: a simple reparameterization to accelerate training of deep neural networks
Tim Salimans, Diederik P. Kingma
2016neural information processing systems617 citations
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PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
2017arXiv (Cornell University)584 citations Open access
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Variational Lossy Autoencoder
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan +4 more
2016arXiv (Cornell University)439 citations Open access
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Imagen Video: High Definition Video Generation with Diffusion Models
Jonathan Ho, Chan, William, Chitwan Saharia, Jay Whang +7 more
2022arXiv (Cornell University)399 citations Open access
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Variational Diffusion Models
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
2021arXiv (Cornell University)374 citations Open access
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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik P. Kingma, Max Welling
2014UvA-DARE (University of Amsterdam)368 citations Open access
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On Distillation of Guided Diffusion Models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma +3 more
2023IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings312 citations
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Improving Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafał Józefowicz, Chen, Xi +2 more
2016arXiv (Cornell University)238 citations Open access
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Stochastic Gradient VB and the Variational Auto-Encoder
Diederik P. Kingma, Max Welling
2013arXiv (Cornell University)234 citations Open access
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Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen
2019arXiv (Cornell University)223 citations Open access
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Wave-Tacotron: Spectrogram-Free End-to-End Text-to-Speech Synthesis
Ron J. Weiss, RJ Skerry-Ryan, Eric Battenberg, Soroosh Mariooryad +1 more
2021IEEE International Conference on Acoustics Speech and Signal Processing84 citations Open access
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How to Train Your Energy-Based Models
Yang Song, Diederik P. Kingma
2021arXiv (Cornell University)84 citations Open access
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Score-Based Generative Modeling through Stochastic Differential Equations
Yang Sup Song, Jascha Sohl‐Dickstein, Diederik P. Kingma, Abhishek Kumar +2 more
2021International Conference on Learning Representations65 citations
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Flow Contrastive Estimation of Energy-Based Models
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xing Xu +2 more
2020IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings60 citations Open access
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Regularized estimation of image statistics by Score Matching
Diederik P. Kingma, Yann LeCun
2010neural information processing systems52 citations
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Variational inference & deep learning: A new synthesis
Diederik P. Kingma
201744 citations
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Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Sup Song, Ben Poole, Ying Wu +1 more
2020arXiv (Cornell University)40 citations Open access
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Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation
Diederik P. Kingma, Ruiqi Gao
2023neural information processing systems39 citations Open access
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Improving Variational Autoencoders with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafał Józefowicz, Xi Chen +2 more
2016Data Archiving and Networked Services (DANS)38 citations Open access
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Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Diederik P. Kingma, Max Welling
2014UvA-DARE (University of Amsterdam)35 citations Open access
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ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
2020arXiv (Cornell University)29 citations Open access
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Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form
Diederik P. Kingma
2013arXiv (Cornell University)26 citations Open access
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On Linear Identifiability of Learned Representations
Geoffrey Roeder, Luke Metz, Diederik P. Kingma
2020arXiv (Cornell University)21 citations Open access
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ICE-BeeM: Identifiable Conditional Energy-Based Deep Models.
Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen
2020Työväentutkimus Vuosikirja13 citations Open access
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On Distillation of Guided Diffusion Models
Chenlin Meng, Rombach, Robin, Ruiqi Gao, Diederik P. Kingma +3 more
2022arXiv (Cornell University)9 citations Open access
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On Density Estimation with Diffusion Models
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
2021neural information processing systems3 citations
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EM Distillation for One-step Diffusion Models
Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou +5 more
2024neural information processing systems1 citations Open access
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Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models
Jascha Sohl‐Dickstein, Diederik P. Kingma
2015arXiv (Cornell University)1 citations Open access
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A Trustworthy AI Framework for Intrusion Detection with Real-Time Decision Interpretability
Yoshua Bengio, Pierre-Antoine Manzagol, Diederik P. Kingma, Max Welling +3 more
20260 citations Open access
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Most-cited papers