Tim Salimans

75 papers A* 18A 1Journal 46Unranked 10
YearRankTypeTitle / Venue / Authors
2026 J jnl
Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD.
CoRR
Emiel Hoogeboom, David Ruhe, Jonathan Heek, Thomas Mensink, Tim Salimans
2026 J jnl
Unified Latents (UL): How to train your latents.
CoRR
Jonathan Heek, Emiel Hoogeboom, Thomas Mensink, Tim Salimans
2025 A* conf
Simpler Diffusion: 1.5 FID on ImageNet512 with Pixel-space Diffusion.
CVPR
Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, Tim Salimans
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
2024 J jnl
Multistep Consistency Models.
CoRR
Jonathan Heek, Emiel Hoogeboom, Tim Salimans
2024 A* conf
Multistep Distillation of Diffusion Models via Moment Matching.
NeurIPS
Tim Salimans, Thomas Mensink, Jonathan Heek, Emiel Hoogeboom
2024 J jnl
Multistep Distillation of Diffusion Models via Moment Matching.
CoRR
Tim Salimans, Thomas Mensink, Jonathan Heek, Emiel Hoogeboom
2024 A* conf
Rolling Diffusion Models.
ICML
David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom
2024 J jnl
Rolling Diffusion Models.
CoRR
David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom
2024 J jnl
Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion.
CoRR
Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, Tim Salimans
2023 A* conf
Blurring Diffusion Models.
ICLR
Emiel Hoogeboom, Tim Salimans
2023 A* conf
Discrete Predictor-Corrector Diffusion Models for Image Synthesis.
ICLR
José Lezama, Tim Salimans, Lu Jiang, Huiwen Chang, Jonathan Ho, Irfan Essa
2023 J jnl
Image Super-Resolution via Iterative Refinement.
IEEE Trans. Pattern Anal. Mach. Intell.
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, Mohammad Norouzi
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
simple diffusion: End-to-end diffusion for high resolution images.
ICML
Emiel Hoogeboom, Jonathan Heek, Tim Salimans
2023 J jnl
simple diffusion: End-to-end diffusion for high resolution images.
CoRR
Emiel Hoogeboom, Jonathan Heek, Tim Salimans
2022 A* conf
Autoregressive Diffusion Models.
ICLR
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans
2022 J jnl
Blurring Diffusion Models.
CoRR
Emiel Hoogeboom, Tim Salimans
2022 J jnl
Cascaded Diffusion Models for High Fidelity Image Generation.
J. Mach. Learn. Res.
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans
2022 J jnl
Classifier-Free Diffusion Guidance.
CoRR
Jonathan Ho, Tim Salimans
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
Lossy Compression with Gaussian Diffusion.
CoRR
Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer
2022 — conf
Milking CowMask for Semi-supervised Image Classification.
VISIGRAPP (5: VISAPP)
Geoff French, Avital Oliver, 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
2022 — conf
Palette: Image-to-Image Diffusion Models.
SIGGRAPH (Conference Paper Track)
Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, Mohammad Norouzi
2022 A* conf
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.
NeurIPS
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, Mohammad Norouzi
2022 J jnl
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.
CoRR
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Raphael Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, Mohammad Norouzi
2022 A* conf
Progressive Distillation for Fast Sampling of Diffusion Models.
ICLR
Tim Salimans, Jonathan Ho
2022 J jnl
Progressive Distillation for Fast Sampling of Diffusion Models.
CoRR
Tim Salimans, Jonathan Ho
2022 A* conf
Video Diffusion Models.
NeurIPS
Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet
2022 J jnl
Video Diffusion Models.
CoRR
Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet
2021 J jnl
Agent-Centric Representations for Multi-Agent Reinforcement Learning.
CoRR
Wenling Shang, Lasse Espeholt, Anton Raichuk, Tim Salimans
2021 J jnl
Autoregressive Diffusion Models.
CoRR
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans
2021 J jnl
Cascaded Diffusion Models for High Fidelity Image Generation.
CoRR
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans
2021 A* conf
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression.
ICLR
Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans
2021 J jnl
Image Super-Resolution via Iterative Refinement.
CoRR
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, Mohammad Norouzi
2021 A* conf
On Density Estimation with Diffusion Models.
NeurIPS
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
2021 J jnl
Palette: Image-to-Image Diffusion Models.
CoRR
Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, Mohammad Norouzi
2021 J jnl
Variational Diffusion Models.
CoRR
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho
2020 A* conf
A Spectral Energy Distance for Parallel Speech Synthesis.
NeurIPS
Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner
2020 J jnl
A Spectral Energy Distance for Parallel Speech Synthesis.
CoRR
Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner
2020 A* conf
How Good is the Bayes Posterior in Deep Neural Networks Really?
ICML
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020 J jnl
How Good is the Bayes Posterior in Deep Neural Networks Really?
CoRR
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020 J jnl
Hydra: Preserving Ensemble Diversity for Model Distillation.
CoRR
Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton
2020 J jnl
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression.
CoRR
Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans
2020 J jnl
MetNet: A Neural Weather Model for Precipitation Forecasting.
CoRR
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal, Jason Hickey, Nal Kalchbrenner
2020 J jnl
Milking CowMask for Semi-Supervised Image Classification.
CoRR
Geoff French, Avital Oliver, Tim Salimans
2020 A* conf
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks.
ICML
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020 J jnl
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks.
CoRR
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2019 J jnl
Axial Attention in Multidimensional Transformers.
CoRR
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans
2019 J jnl
Dota 2 with Large Scale Deep Reinforcement Learning.
CoRR
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Christopher Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Susan Zhang
2019 — conf
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders.
DGS@ICLR
Alexey A. Gritsenko, Jasper Snoek, Tim Salimans
2019 J jnl
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees.
CoRR
Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman
2018 — conf
Improving GANs Using Optimal Transport.
ICLR (Poster)
Tim Salimans, Han Zhang, Alec Radford, Dimitris N. Metaxas
2018 J jnl
Improving GANs Using Optimal Transport.
CoRR
Tim Salimans, Han Zhang, Alec Radford, Dimitris N. Metaxas
2018 J jnl
Learning Montezuma's Revenge from a Single Demonstration.
CoRR
Tim Salimans, Richard Chen
2017 J jnl
Evolution Strategies as a Scalable Alternative to Reinforcement Learning.
CoRR
Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever
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 J jnl
A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features.
CoRR
Tim Salimans
2016 — conf
Improved Techniques for Training GANs.
NIPS
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
2016 J jnl
Improved Techniques for Training GANs.
CoRR
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
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 A* conf
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap.
ICML
Tim Salimans, Diederik P. Kingma, Max Welling
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
2014 J jnl
Observing Dark Worlds: A crowdsourcing experiment for dark matter mapping.
Astron. Comput.
David R. Harvey, Thomas D. Kitching, Joyce Noah-Vanhoucke, Ben Hamner, Tim Salimans, Ana M. Pires
2012 A conf
Collaborative learning of preference rankings.
RecSys
Tim Salimans, Ulrich Paquet, Thore Graepel
2012 J jnl
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
CoRR
Tim Salimans, David A. Knowles