| Improving language understanding by generative pre-training. OpenAI A Radford, K Narasimhan, T Salimans, I Sutskever Preprint, 1-12, 2018 | 13176* | 2018 |
| 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 | 11750 | 2016 |
| Photorealistic text-to-image diffusion models with deep language understanding C Saharia, W Chan, S Saxena, L Li, J Whang, EL Denton, K Ghasemipour, ... Advances in neural information processing systems 35, 36479-36494, 2022 | 5967 | 2022 |
| Classifier-free diffusion guidance J Ho, T Salimans arXiv preprint arXiv:2207.12598, 2022 | 3809 | 2022 |
| Weight normalization: A simple reparameterization to accelerate training of deep neural networks T Salimans, DP Kingma Advances in neural information processing systems 29, 2016 | 2441 | 2016 |
| 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 | 2297 | 2019 |
| 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 | 2269 | 2016 |
| 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 45 (4), 4713-4726, 2022 | 1919 | 2022 |
| 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 | 1900 | 2017 |
| Variational dropout and the local reparameterization trick DP Kingma, T Salimans, M Welling Advances in neural information processing systems 28, 2015 | 1884 | 2015 |
| Video diffusion models J Ho, T Salimans, A Gritsenko, W Chan, M Norouzi, DJ Fleet Advances in Neural Information Processing Systems 35, 8633-8646, 2022 | 1570 | 2022 |
| Palette: Image-to-image diffusion models C Saharia, W Chan, H Chang, C Lee, J Ho, T Salimans, D Fleet, ... ACM SIGGRAPH 2022 conference proceedings, 1-10, 2022 | 1525 | 2022 |
| 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 | 1235 | 2017 |
| Progressive distillation for fast sampling of diffusion models T Salimans, J Ho International Conference on Learning Representations, 2022 | 1226 | 2022 |
| Cascaded diffusion models for high fidelity image generation J Ho, C Saharia, W Chan, DJ Fleet, M Norouzi, T Salimans Journal of Machine Learning Research 23 (47), 1-33, 2022 | 1212 | 2022 |
| Variational diffusion models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021 | 1156 | 2021 |
| Improving language understanding by generative pre-training.(2018) A Radford, K Narasimhan, T Salimans, I Sutskever | 1138 | 2018 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 850 | 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 | 755 | 2015 |
| Axial attention in multidimensional transformers J Ho, N Kalchbrenner, D Weissenborn, T Salimans arXiv preprint arXiv:1912.12180, 2019 | 673 | 2019 |
