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Tim Salimans
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Year
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
78112016
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
5319*2018
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T Salimans, DP Kingma
Advances in neural information processing systems 29, 2016
17072016
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
16022016
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
13302017
Variational dropout and the local reparameterization trick
DP Kingma, T Salimans, M Welling
Advances in neural information processing systems 28, 2015
12952015
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
10622019
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
8402017
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
6522016
Markov chain monte carlo and variational inference: Bridging the gap
T Salimans, D Kingma, M Welling
International conference on machine learning, 1218-1226, 2015
5812015
Photorealistic text-to-image diffusion models with deep language understanding
C Saharia, W Chan, S Saxena, L Li, J Whang, E Denton, ...
arXiv preprint arXiv:2205.11487, 2022
3372022
Axial attention in multidimensional transformers
J Ho, N Kalchbrenner, D Weissenborn, T Salimans
arXiv preprint arXiv:1912.12180, 2019
2652019
Improving GANs Using Optimal Transport
T Salimans, H Zhang, A Radford, D Metaxas
International Conference on Learning Representations (ICLR), 2018
2522018
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
2402020
Fixed-form variational posterior approximation through stochastic linear regression
T Salimans, DA Knowles
2292013
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, 2022
1812022
Classifier-free diffusion guidance
J Ho, T Salimans
arXiv preprint arXiv:2207.12598, 2022
1712022
Variational diffusion models
D Kingma, T Salimans, B Poole, J Ho
Advances in neural information processing systems 34, 21696-21707, 2021
1602021
Metnet: A neural weather model for precipitation forecasting
CK Sønderby, L Espeholt, J Heek, M Dehghani, A Oliver, T Salimans, ...
arXiv preprint arXiv:2003.12140, 2020
1482020
Cascaded Diffusion Models for High Fidelity Image Generation.
J Ho, C Saharia, W Chan, DJ Fleet, M Norouzi, T Salimans
J. Mach. Learn. Res. 23 (47), 1-33, 2022
1372022
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