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Tim Salimans
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Cited by
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
66692016
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
3957*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
14582016
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
13732016
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
11452017
Variational dropout and the local reparameterization trick
DP Kingma, T Salimans, M Welling
Advances in neural information processing systems 28, 2015
10722015
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
7062019
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
6822017
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
5812016
Markov chain monte carlo and variational inference: Bridging the gap
T Salimans, D Kingma, M Welling
International Conference on Machine Learning, 1218-1226, 2015
5122015
Improving GANs Using Optimal Transport
T Salimans, H Zhang, A Radford, D Metaxas
International Conference on Learning Representations (ICLR), 2018
2172018
Fixed-form variational posterior approximation through stochastic linear regression
T Salimans, DA Knowles
Bayesian Analysis 8 (4), 837-882, 2013
2122013
Axial attention in multidimensional transformers
J Ho, N Kalchbrenner, D Weissenborn, T Salimans
arXiv preprint arXiv:1912.12180, 2019
1522019
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
1382020
Learning Montezuma’s Revenge from a single demonstration
T Salimans, R Chen
Deep RL Workshop, Neural Information Processing Systems (NeurIPS), 2018
892018
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
822020
Dota 2 with large scale deep reinforcement learning
CB OpenAI, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ...
arXiv preprint arXiv:1912.06680 2, 2019
522019
Variational diffusion models
DP Kingma, T Salimans, B Poole, J Ho
arXiv preprint arXiv:2107.00630, 2021
402021
Image super-resolution via iterative refinement
C Saharia, J Ho, W Chan, T Salimans, DJ Fleet, M Norouzi
arXiv preprint arXiv:2104.07636, 2021
402021
Variable selection and functional form uncertainty in cross-country growth regressions
T Salimans
Journal of Econometrics 171 (2), 267-280, 2012
292012
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