| 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 | 890 | 2022 |
| On distillation of guided diffusion models C Meng, R Rombach, R Gao, D Kingma, S Ermon, J Ho, T Salimans Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 256 | 2023 |
| Learning Descriptor Networks for 3D Shape Synthesis and Analysis J Xie, Z Zheng, R Gao, W Wang, SC Zhu, YN Wu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 160 | 2018 |
| Cooperative training of descriptor and generator networks J Xie, Y Lu, R Gao, SC Zhu, YN Wu IEEE transactions on pattern analysis and machine intelligence 42 (1), 27-45, 2018 | 152 | 2018 |
| Learning Energy-Based Models by Diffusion Recovery Likelihood R Gao, Y Song, B Poole, YN Wu, DP Kingma arXiv preprint arXiv:2012.08125, 2020 | 113 | 2020 |
| Flow contrastive estimation of energy-based models R Gao, E Nijkamp, DP Kingma, Z Xu, AM Dai, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 109 | 2020 |
| Cooperative learning of energy-based model and latent variable model via mcmc teaching J Xie, Y Lu, R Gao, YN Wu Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 88 | 2018 |
| Learning generative convnets via multi-grid modeling and sampling R Gao, Y Lu, J Zhou, SC Zhu, YN Wu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 83 | 2018 |
| Latent diffusion energy-based model for interpretable text modeling P Yu, S Xie, X Ma, B Jia, B Pang, R Gao, Y Zhu, SC Zhu, YN Wu arXiv preprint arXiv:2206.05895, 2022 | 62 | 2022 |
| Generative VoxelNet: learning energy-based models for 3D shape synthesis and analysis J Xie, Z Zheng, R Gao, W Wang, SC Zhu, YN Wu IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (5), 2468-2484, 2020 | 48 | 2020 |
| Learning grid cells as vector representation of self-position coupled with matrix representation of self-motion R Gao, J Xie, SC Zhu, YN Wu arXiv preprint arXiv:1810.05597, 2018 | 41 | 2018 |
| Learning dynamic generator model by alternating back-propagation through time J Xie, R Gao, Z Zheng, SC Zhu, YN Wu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5498-5507, 2019 | 38 | 2019 |
| Unsupervised disentangling of appearance and geometry by deformable generator network X Xing, T Han, R Gao, SC Zhu, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 33 | 2019 |
| Deformable generator networks: unsupervised disentanglement of appearance and geometry X Xing, R Gao, T Han, SC Zhu, YN Wu IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (3), 1162-1179, 2020 | 32 | 2020 |
| Understanding diffusion objectives as the ELBO with simple data augmentation D Kingma, R Gao Advances in Neural Information Processing Systems 36, 2024 | 26 | 2024 |
| ReconFusion: 3D Reconstruction with Diffusion Priors R Wu, B Mildenhall, P Henzler, K Park, R Gao, D Watson, PP Srinivasan, ... arXiv preprint arXiv:2312.02981, 2023 | 21 | 2023 |
| MCMC should mix: learning energy-based model with neural transport latent space MCMC. E Nijkamp, R Gao, P Sountsov, S Vasudevan, B Pang, SC Zhu, YN Wu International Conference on Learning Representations (ICLR 2022)., 2022 | 21 | 2022 |
| Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC E Nijkamp, R Gao, P Sountsov, S Vasudevan, B Pang, SC Zhu, YN Wu arXiv preprint arXiv:2006.06897, 2020 | 21 | 2020 |
| A tale of three probabilistic families: Discriminative, descriptive, and generative models YN Wu, R Gao, T Han, SC Zhu Quarterly of Applied Mathematics 77 (2), 423-465, 2019 | 21 | 2019 |
| A remark on copy number variation detection methods S Li, X Dou, R Gao, X Ge, M Qian, L Wan PloS one 13 (4), e0196226, 2018 | 19 | 2018 |
Ying Nian WuUCLA Department of Statistics在 stat.ucla.edu 的电子邮件经过验证
