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, 2234-2242, 2016
40892016
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
1676*2018
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T Salimans, DP Kingma
Advances in neural information processing systems, 901-909, 2016
9222016
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, 4743-4751, 2016
8682016
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
6832017
Variational dropout and the local reparameterization trick
DP Kingma, T Salimans, M Welling
Advances in neural information processing systems, 2575-2583, 2015
6022015
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
3912017
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
3912016
Markov chain monte carlo and variational inference: Bridging the gap
T Salimans, D Kingma, M Welling
International Conference on Machine Learning, 1218-1226, 2015
3652015
Fixed-form variational posterior approximation through stochastic linear regression
T Salimans, DA Knowles
Bayesian Analysis 8 (4), 837-882, 2013
1722013
Improving GANs Using Optimal Transport
T Salimans, H Zhang, A Radford, D Metaxas
International Conference on Learning Representations (ICLR), 2018
1212018
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
98*2019
Learning Montezuma’s Revenge from a single demonstration
T Salimans, R Chen
Deep RL Workshop, Neural Information Processing Systems (NeurIPS), 2018
472018
Variable selection and functional form uncertainty in cross-country growth regressions
T Salimans
Journal of Econometrics 171 (2), 267-280, 2012
252012
On using control variates with stochastic approximation for variational bayes and its connection to stochastic linear regression
T Salimans, DA Knowles
arXiv preprint arXiv:1401.1022, 2014
162014
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
142020
The likelihood of mixed hitting times
JH Abbring, T Salimans
arXiv preprint arXiv:1905.03463, 2019
132019
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
13*2016
Collaborative learning of preference rankings
T Salimans, U Paquet, T Graepel
Proceedings of the sixth ACM conference on Recommender systems, 261-264, 2012
13*2012
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
122019
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Articles 1–20