| 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 | 8967 | 2016 |
| Improving language understanding by generative pre-training A Radford, K Narasimhan, T Salimans, I Sutskever | 7208 | 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 | 1952 | 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, 2016 | 1820 | 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 | 1515 | 2017 |
| 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 | 1482 | 2022 |
| Variational dropout and the local reparameterization trick DP Kingma, T Salimans, M Welling Advances in neural information processing systems 28, 2015 | 1458 | 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 | 1394 | 2019 |
| 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 | 970 | 2017 |
| Classifier-free diffusion guidance J Ho, T Salimans arXiv preprint arXiv:2207.12598, 2022 | 719 | 2022 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 713 | 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 | 655 | 2015 |
| 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 | 537 | 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 | 444 | 2022 |
| Cascaded diffusion models for high fidelity image generation J Ho, C Saharia, W Chan, DJ Fleet, M Norouzi, T Salimans The Journal of Machine Learning Research 23 (1), 2249-2281, 2022 | 405 | 2022 |
| Variational diffusion models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021 | 376 | 2021 |
| Axial attention in multidimensional transformers J Ho, N Kalchbrenner, D Weissenborn, T Salimans arXiv preprint arXiv:1912.12180, 2019 | 373 | 2019 |
| 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 | 289 | 2020 |
| Improving GANs Using Optimal Transport T Salimans, H Zhang, A Radford, D Metaxas International Conference on Learning Representations (ICLR), 2018 | 288 | 2018 |
| Imagen video: High definition video generation with diffusion models J Ho, W Chan, C Saharia, J Whang, R Gao, A Gritsenko, DP Kingma, ... arXiv preprint arXiv:2210.02303, 2022 | 260 | 2022 |
