| On the anatomy of mcmc-based maximum likelihood learning of energy-based models E Nijkamp, M Hill, T Han, SC Zhu, YN Wu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5272-5280, 2020 | 178 | 2020 |
| Alternating back-propagation for generator network T Han, Y Lu, SC Zhu, YN Wu Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 155 | 2017 |
| Learning latent space energy-based prior model B Pang, T Han, E Nijkamp, SC Zhu, YN Wu Advances in Neural Information Processing Systems 33, 21994-22008, 2020 | 144 | 2020 |
| Divergence triangle for joint training of generator model, energy-based model, and inferential model T Han, E Nijkamp, X Fang, M Hill, SC Zhu, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 72 | 2019 |
| Survey on person re‐identification based on deep learning K Wang, H Wang, M Liu, X Xing, T Han CAAI Transactions on Intelligence Technology 3 (4), 219-227, 2018 | 71 | 2018 |
| Joint training of variational auto-encoder and latent energy-based model T Han, E Nijkamp, L Zhou, B Pang, SC Zhu, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 52 | 2020 |
| Learning multi-layer latent variable model via variational optimization of short run mcmc for approximate inference E Nijkamp, B Pang, T Han, L Zhou, SC Zhu, YN Wu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 50 | 2020 |
| Neuro-symbolic program search for autonomous driving decision module design J Sun, H Sun, T Han, B Zhou Conference on Robot Learning, 21-30, 2021 | 39 | 2021 |
| 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 | 35 | 2020 |
| Parsing façade with rank-one approximation C Yang, T Han, L Quan, CL Tai 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1720-1727, 2012 | 35 | 2012 |
| Context-aware health event prediction via transition functions on dynamic disease graphs C Lu, T Han, Y Ning Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4567-4574, 2022 | 34 | 2022 |
| Star: Sparse transformer-based action recognition F Shi, C Lee, L Qiu, Y Zhao, T Shen, S Muralidhar, T Han, SC Zhu, ... arXiv preprint arXiv:2107.07089, 2021 | 33 | 2021 |
| 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 |
| 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 | 22 | 2019 |
| Lavs: A lightweight audio-visual saliency prediction model D Zhu, D Zhao, X Min, T Han, Q Zhou, S Yu, Y Chen, G Zhai, X Yang 2021 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2021 | 13 | 2021 |
| Adaptive multi-stage density ratio estimation for learning latent space energy-based model Z Xiao, T Han Advances in Neural Information Processing Systems 35, 21590-21601, 2022 | 12 | 2022 |
| Learning latent space energy-based prior model for molecule generation B Pang, T Han, YN Wu arXiv preprint arXiv:2010.09351, 2020 | 11 | 2020 |
| Learning generator networks for dynamic patterns T Han, Y Lu, J Wu, X Xing, YN Wu 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 809-818, 2019 | 10 | 2019 |
| Learning energy-based model via dual-MCMC teaching J Cui, T Han Advances in Neural Information Processing Systems 36, 28861-28872, 2023 | 8 | 2023 |
| Learning joint latent space ebm prior model for multi-layer generator J Cui, YN Wu, T Han Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 8 | 2023 |
Ying Nian WuUCLA Department of StatisticsVerified email at stat.ucla.edu
