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ORCIDDiederik P. Kingma
Person information
- affiliation: Google Research, San Francisco, CA, USA
- affiliation (former): OpenAI, San Francisco, CA, USA
- affiliation (PhD 2017): University of Amsterdam, The Netherlands
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2020 – today
- 2024
[i25]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 abs/2405.16852 (2024)- 2023
- [c23]Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans:
On Distillation of Guided Diffusion Models. CVPR 2023: 14297-14306 - [c22]Diederik P. Kingma, Ruiqi Gao:
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation. NeurIPS 2023 - [i24]Diederik P. Kingma, Ruiqi Gao:
Understanding the Diffusion Objective as a Weighted Integral of ELBOs. CoRR abs/2303.00848 (2023) - 2022
- [i23]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 abs/2210.02303 (2022) - [i22]Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans:
On Distillation of Guided Diffusion Models. CoRR abs/2210.03142 (2022) - 2021
- [c21]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: 5679-5683 - [c20]Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. ICLR 2021 - [c19]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR 2021 - [c18]Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho:
On Density Estimation with Diffusion Models. NeurIPS 2021: 21696-21707 - [i21]Yang Song, Diederik P. Kingma:
How to Train Your Energy-Based Models. CoRR abs/2101.03288 (2021) - [i20]Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho:
Variational Diffusion Models. CoRR abs/2107.00630 (2021) - 2020
- [c17]Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen:
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. AISTATS 2020: 2207-2217 - [c16]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CVPR 2020: 7515-7525 - [c15]Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen:
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. NeurIPS 2020 - [i19]Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen:
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. CoRR abs/2002.11537 (2020) - [i18]Geoffrey Roeder, Luke Metz, Diederik P. Kingma:
On Linear Identifiability of Learned Representations. CoRR abs/2007.00810 (2020) - [i17]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 abs/2011.03568 (2020) - [i16]Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. CoRR abs/2011.13456 (2020) - [i15]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR abs/2012.08125 (2020)
2010 – 2019
- 2019
- [j1]Diederik P. Kingma, Max Welling:
An Introduction to Variational Autoencoders. Found. Trends Mach. Learn. 12(4): 307-392 (2019) - [i14]Diederik P. Kingma, Max Welling:
An Introduction to Variational Autoencoders. CoRR abs/1906.02691 (2019) - [i13]Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen:
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. CoRR abs/1907.04809 (2019) - [i12]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CoRR abs/1912.00589 (2019) - 2018
- [c14]Christos Louizos, Max Welling, Diederik P. Kingma:
Learning Sparse Neural Networks through L_0 Regularization. ICLR (Poster) 2018 - [c13]Diederik P. Kingma, Prafulla Dhariwal:
Glow: Generative Flow with Invertible 1x1 Convolutions. NeurIPS 2018: 10236-10245 - [i11]Diederik P. Kingma, Prafulla Dhariwal:
Glow: Generative Flow with Invertible 1x1 Convolutions. CoRR abs/1807.03039 (2018) - 2017
- [c12]Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. ICLR (Poster) 2017 - [c11]Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma:
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. ICLR (Poster) 2017 - [i10]Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma:
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. CoRR abs/1701.05517 (2017) - [i9]Christos Louizos, Max Welling, Diederik P. Kingma:
Learning Sparse Neural Networks through L0 Regularization. CoRR abs/1712.01312 (2017) - 2016
- [c10]Tim Salimans, Diederik P. Kingma:
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. NIPS 2016: 901 - [c9]Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling:
Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS 2016: 4736-4744 - [i8]Tim Salimans, Diederik P. Kingma:
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. CoRR abs/1602.07868 (2016) - [i7]Diederik P. Kingma, Tim Salimans, Max Welling:
Improving Variational Inference with Inverse Autoregressive Flow. CoRR abs/1606.04934 (2016) - [i6]Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. CoRR abs/1611.02731 (2016) - 2015
- [c8]Tim Salimans, Diederik P. Kingma, Max Welling:
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. ICML 2015: 1218-1226 - [c7]Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. NIPS 2015: 2575-2583 - [c6]Otto Fabius, Joost R. van Amersfoort, Diederik P. Kingma:
Variational Recurrent Auto-Encoders. ICLR (Workshop) 2015 - [c5]Diederik P. Kingma, Jimmy Ba:
Adam: A Method for Stochastic Optimization. ICLR (Poster) 2015 - [i5]Jascha Sohl-Dickstein, Diederik P. Kingma:
Technical Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models. CoRR abs/1504.08025 (2015) - [i4]Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. CoRR abs/1506.02557 (2015) - 2014
- [c4]Diederik P. Kingma, Max Welling:
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. ICML 2014: 1782-1790 - [c3]Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling:
Semi-supervised Learning with Deep Generative Models. NIPS 2014: 3581-3589 - [c2]Diederik P. Kingma, Max Welling:
Auto-Encoding Variational Bayes. ICLR 2014 - [i3]Diederik P. Kingma, Max Welling:
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. CoRR abs/1402.0480 (2014) - [i2]Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling:
Semi-Supervised Learning with Deep Generative Models. CoRR abs/1406.5298 (2014) - 2013
- [i1]Diederik P. Kingma:
Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form. CoRR abs/1306.0733 (2013) - 2010
- [c1]Diederik P. Kingma, Yann LeCun:
Regularized estimation of image statistics by Score Matching. NIPS 2010: 1126-1134
Coauthor Index
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