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Tim Salimans
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2020 – today
- 2024
- [c26]David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom:
Rolling Diffusion Models. ICML 2024 - [i40]David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom:
Rolling Diffusion Models. CoRR abs/2402.09470 (2024) - [i39]Jonathan Heek, Emiel Hoogeboom, Tim Salimans:
Multistep Consistency Models. CoRR abs/2403.06807 (2024) - [i38]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) - [i37]Tim Salimans, Thomas Mensink, Jonathan Heek, Emiel Hoogeboom:
Multistep Distillation of Diffusion Models via Moment Matching. CoRR abs/2406.04103 (2024) - 2023
- [j3]Chitwan Saharia, Jonathan Ho, William Chan
, Tim Salimans, David J. Fleet
Image Super-Resolution via Iterative Refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4713-4726 (2023) - [c25]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 - [c24]Emiel Hoogeboom, Tim Salimans:
Blurring Diffusion Models. ICLR 2023 - [c23]José Lezama, Tim Salimans, Lu Jiang, Huiwen Chang, Jonathan Ho, Irfan Essa:
Discrete Predictor-Corrector Diffusion Models for Image Synthesis. ICLR 2023 - [c22]Emiel Hoogeboom, Jonathan Heek, Tim Salimans:
simple diffusion: End-to-end diffusion for high resolution images. ICML 2023: 13213-13232 - [i36]Emiel Hoogeboom, Jonathan Heek, Tim Salimans:
simple diffusion: End-to-end diffusion for high resolution images. CoRR abs/2301.11093 (2023) - 2022
- [j2]Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans:
Cascaded Diffusion Models for High Fidelity Image Generation. J. Mach. Learn. Res. 23: 47:1-47:33 (2022) - [c21]Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans:
Autoregressive Diffusion Models. ICLR 2022 - [c20]Tim Salimans, Jonathan Ho:
Progressive Distillation for Fast Sampling of Diffusion Models. ICLR 2022 - [c19]Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet:
Video Diffusion Models. NeurIPS 2022 - [c18]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:
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. NeurIPS 2022 - [c17]Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, Mohammad Norouzi:
Palette: Image-to-Image Diffusion Models. SIGGRAPH (Conference Paper Track) 2022: 15:1-15:10 - [c16]Geoff French, Avital Oliver, Tim Salimans:
Milking CowMask for Semi-supervised Image Classification. VISIGRAPP (5: VISAPP) 2022: 75-84 - [i35]Tim Salimans, Jonathan Ho:
Progressive Distillation for Fast Sampling of Diffusion Models. CoRR abs/2202.00512 (2022) - [i34]Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet:
Video Diffusion Models. CoRR abs/2204.03458 (2022) - [i33]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:
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. CoRR abs/2205.11487 (2022) - [i32]Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer:
Lossy Compression with Gaussian Diffusion. CoRR abs/2206.08889 (2022) - [i31]Jonathan Ho, Tim Salimans:
Classifier-Free Diffusion Guidance. CoRR abs/2207.12598 (2022) - [i30]Emiel Hoogeboom, Tim Salimans:
Blurring Diffusion Models. CoRR abs/2209.05557 (2022) - [i29]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) - [i28]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
- [c15]Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans:
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression. ICLR 2021 - [c14]Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho:
On Density Estimation with Diffusion Models. NeurIPS 2021: 21696-21707 - [i27]Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, Mohammad Norouzi:
Image Super-Resolution via Iterative Refinement. CoRR abs/2104.07636 (2021) - [i26]Wenling Shang, Lasse Espeholt, Anton Raichuk, Tim Salimans:
Agent-Centric Representations for Multi-Agent Reinforcement Learning. CoRR abs/2104.09402 (2021) - [i25]Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans:
Cascaded Diffusion Models for High Fidelity Image Generation. CoRR abs/2106.15282 (2021) - [i24]Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho:
Variational Diffusion Models. CoRR abs/2107.00630 (2021) - [i23]Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans:
Autoregressive Diffusion Models. CoRR abs/2110.02037 (2021) - [i22]Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, Mohammad Norouzi:
Palette: Image-to-Image Diffusion Models. CoRR abs/2111.05826 (2021) - 2020
- [c13]Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. ICML 2020: 9289-9299 - [c12]Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020: 10248-10259 - [c11]Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner:
A Spectral Energy Distance for Parallel Speech Synthesis. NeurIPS 2020 - [i21]Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton:
Hydra: Preserving Ensemble Diversity for Model Distillation. CoRR abs/2001.04694 (2020) - [i20]Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
How Good is the Bayes Posterior in Deep Neural Networks Really? CoRR abs/2002.02405 (2020) - [i19]Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. CoRR abs/2002.02655 (2020) - [i18]Geoff French, Avital Oliver, Tim Salimans:
Milking CowMask for Semi-Supervised Image Classification. CoRR abs/2003.12022 (2020) - [i17]Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal, Jason Hickey, Nal Kalchbrenner:
MetNet: A Neural Weather Model for Precipitation Forecasting. CoRR abs/2003.12140 (2020) - [i16]Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, Tim Salimans:
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression. CoRR abs/2006.12459 (2020) - [i15]Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner:
A Spectral Energy Distance for Parallel Speech Synthesis. CoRR abs/2008.01160 (2020)
2010 – 2019
- 2019
- [c10]Alexey A. Gritsenko, Jasper Snoek, Tim Salimans:
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders. DGS@ICLR 2019 - [i14]Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman:
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees. CoRR abs/1904.03646 (2019) - [i13]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:
Dota 2 with Large Scale Deep Reinforcement Learning. CoRR abs/1912.06680 (2019) - [i12]Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans:
Axial Attention in Multidimensional Transformers. CoRR abs/1912.12180 (2019) - 2018
- [c9]Tim Salimans, Han Zhang, Alec Radford, Dimitris N. Metaxas:
Improving GANs Using Optimal Transport. ICLR (Poster) 2018 - [i11]Tim Salimans, Han Zhang, Alec Radford, Dimitris N. Metaxas:
Improving GANs Using Optimal Transport. CoRR abs/1803.05573 (2018) - [i10]Tim Salimans, Richard Chen:
Learning Montezuma's Revenge from a Single Demonstration. CoRR abs/1812.03381 (2018) - 2017
- [c8]Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. ICLR (Poster) 2017 - [c7]Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma:
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. ICLR (Poster) 2017 - [i9]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) - [i8]Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever:
Evolution Strategies as a Scalable Alternative to Reinforcement Learning. CoRR abs/1703.03864 (2017) - 2016
- [c6]Tim Salimans, Diederik P. Kingma:
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. NIPS 2016: 901 - [c5]Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen:
Improved Techniques for Training GANs. NIPS 2016: 2226-2234 - [c4]Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling:
Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS 2016: 4736-4744 - [i7]Tim Salimans, Diederik P. Kingma:
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. CoRR abs/1602.07868 (2016) - [i6]Tim Salimans:
A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features. CoRR abs/1602.08734 (2016) - [i5]Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen:
Improved Techniques for Training GANs. CoRR abs/1606.03498 (2016) - [i4]Diederik P. Kingma, Tim Salimans, Max Welling:
Improving Variational Inference with Inverse Autoregressive Flow. CoRR abs/1606.04934 (2016) - [i3]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
- [c3]Tim Salimans, Diederik P. Kingma, Max Welling:
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. ICML 2015: 1218-1226 - [c2]Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. NIPS 2015: 2575-2583 - [i2]Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. CoRR abs/1506.02557 (2015) - 2014
- [j1]David R. Harvey, Thomas D. Kitching, Joyce Noah-Vanhoucke, Ben Hamner, Tim Salimans, Ana M. Pires
Observing Dark Worlds: A crowdsourcing experiment for dark matter mapping. Astron. Comput. 5: 35-44 (2014) - 2012
- [c1]Tim Salimans, Ulrich Paquet, Thore Graepel:
Collaborative learning of preference rankings. RecSys 2012: 261-264 - [i1]Tim Salimans, David A. Knowles:
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression. CoRR abs/1206.6679 (2012)
Coauthor Index
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