| 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 | 103 | 2018 |
| 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 | 99 | 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 | 60 | 2018 |
| 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 | 58 | 2018 |
| 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 | 53 | 2020 |
| 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 | 32 | 2019 |
| Learning Energy-Based Models by Diffusion Recovery Likelihood R Gao, Y Song, B Poole, YN Wu, DP Kingma arXiv preprint arXiv:2012.08125, 2020 | 28 | 2020 |
| 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 | 27 | 2019 |
| 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 | 22 | 2018 |
| 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, 2020 | 20 | 2020 |
| 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 | 16 | 2018 |
| 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, 2020 | 15 | 2020 |
| 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 | 13 | 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 | 11 | 2019 |
| Motion-based generator model: Unsupervised disentanglement of appearance, trackable and intrackable motions in dynamic patterns J Xie, R Gao, Z Zheng, SC Zhu, YN Wu Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 12442 …, 2020 | 8 | 2020 |
| Representation Learning: A Statistical Perspective J Xie, R Gao, E Nijkamp, SC Zhu, YN Wu Annual Review of Statistics and Its Application 7, 303-335, 2020 | 6 | 2020 |
| A Representational Model of Grid Cells Based on Matrix Lie Algebras R Gao, J Xie, SC Zhu, YN Wu arXiv preprint arXiv:2006.10259, 2020 | 3* | 2020 |
| Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1 R Gao, J Xie, SC Zhu, YN Wu arXiv preprint arXiv:1902.03871, 2019 | 3* | 2019 |
| Exploring generative perspective of convolutional neural networks by learning random field models Y Lu, R Gao, SC Zhu, YN Wu Statistics and Its Interface 11 (3), 515-529, 2018 | 3 | 2018 |
| On Path Integration of grid cells: isotropic metric, conformal embedding and group representation R Gao, J Xie, XX Wei, SC Zhu, YN Wu | 2 | 2021 |
Ying Nian WuUCLA Department of StatisticsVerified email at stat.ucla.edu
