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Tim Salimans
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Improved techniques for training gans
T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen
Advances in neural information processing systems 29, 2016
92592016
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
78242018
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T Salimans, DP Kingma
Advances in neural information processing systems 29, 2016
20062016
Photorealistic text-to-image diffusion models with deep language understanding
C Saharia, W Chan, S Saxena, L Li, J Whang, EL Denton, K Ghasemipour, ...
Advances in Neural Information Processing Systems 35, 36479-36494, 2022
18932022
Improved variational inference with inverse autoregressive flow
DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling
Advances in neural information processing systems 29, 2016
18822016
Evolution strategies as a scalable alternative to reinforcement learning
T Salimans, J Ho, X Chen, S Sidor, I Sutskever
arXiv preprint arXiv:1703.03864, 2017
15672017
Variational dropout and the local reparameterization trick
DP Kingma, T Salimans, M Welling
Advances in neural information processing systems 28, 2015
15152015
Dota 2 with large scale deep reinforcement learning
C Berner, G Brockman, B Chan, V Cheung, P Dębiak, C Dennison, ...
arXiv preprint arXiv:1912.06680, 2019
14992019
Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
T Salimans, A Karpathy, X Chen, DP Kingma
arXiv preprint arXiv:1701.05517, 2017
9942017
Classifier-free diffusion guidance
J Ho, T Salimans
arXiv preprint arXiv:2207.12598, 2022
9402022
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
7292016
Image super-resolution via iterative refinement
C Saharia, J Ho, W Chan, T Salimans, DJ Fleet, M Norouzi
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4), 4713-4726, 2022
6762022
Markov chain monte carlo and variational inference: Bridging the gap
T Salimans, D Kingma, M Welling
International conference on machine learning, 1218-1226, 2015
6662015
Palette: Image-to-image diffusion models
C Saharia, W Chan, H Chang, C Lee, J Ho, T Salimans, D Fleet, ...
ACM SIGGRAPH 2022 Conference Proceedings, 1-10, 2022
5562022
Cascaded diffusion models for high fidelity image generation
J Ho, C Saharia, W Chan, DJ Fleet, M Norouzi, T Salimans
The Journal of Machine Learning Research 23 (1), 2249-2281, 2022
4902022
Variational diffusion models
D Kingma, T Salimans, B Poole, J Ho
Advances in neural information processing systems 34, 21696-21707, 2021
4582021
Axial attention in multidimensional transformers
J Ho, N Kalchbrenner, D Weissenborn, T Salimans
arXiv preprint arXiv:1912.12180, 2019
3972019
Imagen video: High definition video generation with diffusion models
J Ho, W Chan, C Saharia, J Whang, R Gao, A Gritsenko, DP Kingma, ...
arXiv preprint arXiv:2210.02303, 2022
3702022
Progressive distillation for fast sampling of diffusion models
T Salimans, J Ho
arXiv preprint arXiv:2202.00512, 2022
3412022
How good is the bayes posterior in deep neural networks really?
F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ...
arXiv preprint arXiv:2002.02405, 2020
3092020
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