| 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 | 5780 | 2016 |
| Improving language understanding by generative pre-training A Radford, K Narasimhan, T Salimans, I Sutskever | 3057* | 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 | 1291 | 2016 |
| 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 | 1203 | 2016 |
| 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 | 966 | 2017 |
| Variational dropout and the local reparameterization trick DP Kingma, T Salimans, M Welling Advances in neural information processing systems 28, 2575-2583, 2015 | 897 | 2015 |
| 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 | 575 | 2017 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 510 | 2016 |
| Markov chain monte carlo and variational inference: Bridging the gap T Salimans, D Kingma, M Welling International Conference on Machine Learning, 1218-1226, 2015 | 460 | 2015 |
| 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 | 425 | 2019 |
| Fixed-form variational posterior approximation through stochastic linear regression T Salimans, DA Knowles Bayesian Analysis 8 (4), 837-882, 2013 | 202 | 2013 |
| Improving GANs Using Optimal Transport T Salimans, H Zhang, A Radford, D Metaxas International Conference on Learning Representations (ICLR), 2018 | 178 | 2018 |
| 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 | 85 | 2020 |
| Axial attention in multidimensional transformers J Ho, N Kalchbrenner, D Weissenborn, T Salimans arXiv preprint arXiv:1912.12180, 2019 | 77 | 2019 |
| Learning Montezuma’s Revenge from a single demonstration T Salimans, R Chen Deep RL Workshop, Neural Information Processing Systems (NeurIPS), 2018 | 69 | 2018 |
| 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 | 52 | 2020 |
| Variable selection and functional form uncertainty in cross-country growth regressions T Salimans Journal of Econometrics 171 (2), 267-280, 2012 | 26 | 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 | 22 | 2019 |
| 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 | 16 | 2020 |
