Tim Salimans
Title
Cited by
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, 2234-2242, 2016
58582016
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
3124*2018
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T Salimans, DP Kingma
Advances in neural information processing systems 29, 901-909, 2016
13002016
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, 4743-4751, 2016
12202016
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
9732017
Variational dropout and the local reparameterization trick
DP Kingma, T Salimans, M Welling
Advances in neural information processing systems 28, 2575-2583, 2015
9042015
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
5822017
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
5142016
Markov chain monte carlo and variational inference: Bridging the gap
T Salimans, D Kingma, M Welling
International Conference on Machine Learning, 1218-1226, 2015
4632015
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
4352019
Fixed-form variational posterior approximation through stochastic linear regression
T Salimans, DA Knowles
Bayesian Analysis 8 (4), 837-882, 2013
2042013
Improving GANs Using Optimal Transport
T Salimans, H Zhang, A Radford, D Metaxas
International Conference on Learning Representations (ICLR), 2018
1852018
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
882020
Axial attention in multidimensional transformers
J Ho, N Kalchbrenner, D Weissenborn, T Salimans
arXiv preprint arXiv:1912.12180, 2019
812019
Learning Montezuma’s Revenge from a single demonstration
T Salimans, R Chen
Deep RL Workshop, Neural Information Processing Systems (NeurIPS), 2018
712018
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
532020
Variable selection and functional form uncertainty in cross-country growth regressions
T Salimans
Journal of Econometrics 171 (2), 267-280, 2012
262012
Policy gradient search: Online planning and expert iteration without search trees
T Anthony, R Nishihara, P Moritz, T Salimans, J Schulman
arXiv preprint arXiv:1904.03646, 2019
232019
OpenAI Post on Generative Models
A Karpathy, P Abbeel, G Brockman, P Chen, V Cheung, R Duan, ...
URL https://blog. openai. com/generative-models, 2016
20*2016
The k-tied normal distribution: A compact parameterization of gaussian mean field posteriors in bayesian neural networks
J Swiatkowski, K Roth, B Veeling, L Tran, J Dillon, J Snoek, S Mandt, ...
International Conference on Machine Learning, 9289-9299, 2020
162020
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