| Alice: Towards understanding adversarial learning for joint distribution matching C Li, H Liu, C Chen, Y Pu, L Chen, R Henao, L Carin Advances in neural information processing systems 30, 2017 | 277 | 2017 |
| Vision-Language Pre-Training with Triple Contrastive Learning J Yang, J Duan, S Tran, Y Xu, S Chanda, L Chen, B Zeng, T Chilimbi, ... CVPR 2022, 2022 | 226 | 2022 |
| Contextualized perturbation for textual adversarial attack D Li, Y Zhang, H Peng, L Chen, C Brockett, MT Sun, B Dolan NAACL 2021, 2021 | 216 | 2021 |
| Graph Optimal Transport for Cross-Domain Alignment L Chen, Z Gan, Y Cheng, L Li, L Carin, J Liu ICML 2020, 2020 | 164 | 2020 |
| Adversarial text generation via feature-mover's distance L Chen, S Dai, C Tao, D Shen, Z Gan, H Zhang, Y Zhang, L Carin Advances in Neural Information Processing Systems 2018, 2018 | 163 | 2018 |
| Triangle generative adversarial networks Z Gan, L Chen, W Wang, Y Pu, Y Zhang, H Liu, C Li, L Carin Advances in Neural Information Processing Systems, 5253-5262, 2017 | 155 | 2017 |
| Wasserstein Contrastive Representation Distillation L Chen, Z Gan, D Wang, J Liu, R Henao, L Carin CVPR 2021, 2021 | 99 | 2021 |
| Improving Sequence-to-Sequence Learning via Optimal Transport L Chen, Y Zhang, R Zhang, C Tao, Z Gan, H Zhang, B Li, D Shen, C Chen, ... International Conference on Learning Representations, 2019 | 95 | 2019 |
| Adversarial symmetric variational autoencoder Y Pu, W Wang, R Henao, L Chen, Z Gan, C Li, L Carin Advances in neural information processing systems 30, 2017 | 94 | 2017 |
| Symmetric variational autoencoder and connections to adversarial learning L Chen, S Dai, Y Pu, C Li, Q Su, L Carin AISTATS 2018, 2017 | 78 | 2017 |
| A unified particle-optimization framework for scalable Bayesian sampling C Chen, R Zhang, W Wang, B Li, L Chen arXiv preprint arXiv:1805.11659, 2018 | 77 | 2018 |
| Towards generating long and coherent text with multi-level latent variable models D Shen, A Celikyilmaz, Y Zhang, L Chen, X Wang, J Gao, L Carin ACL 2019, 2019 | 64 | 2019 |
| Continuous-time flows for efficient inference and density estimation C Chen, C Li, L Chen, W Wang, Y Pu, LC Duke International Conference on Machine Learning, 824-833, 2018 | 58 | 2018 |
| Multi-modal Alignment using Representation Codebook J Duan, L Chen, S Tran, J Yang, Y Xu, B Zeng, C Tao, T Chilimbi CVPR 2022, 2022 | 52 | 2022 |
| Chi-square generative adversarial network C Tao, L Chen, R Henao, J Feng, L Carin International Conference on Machine Learning, 4894-4903, 2018 | 43 | 2018 |
| Variational inference and model selection with generalized evidence bounds L Chen, C Tao, R Zhang, R Henao, L Carin International Conference on Machine Learning, 892-901, 2018 | 32 | 2018 |
| Improving Textual Network Embedding with Global Attention via Optimal Transport L Chen, G Wang, C Tao, D Shen, P Cheng, X Zhang, W Wang, Y Zhang, ... ACL 2019, 2019 | 29 | 2019 |
| Simpler, faster, stronger: Breaking the log-k curse on contrastive learners with flatnce J Chen, Z Gan, X Li, Q Guo, L Chen, S Gao, T Chung, Y Xu, B Zeng, W Lu, ... arXiv preprint arXiv:2107.01152, 2021 | 26 | 2021 |
| On Fenchel Mini-Max Learning C Tao, L Chen, S Dai, J Chen, K Bai, D Wang, J Feng, W Lu, G Bobashev, ... International Conference on Machine Learning, 2019 | 22 | 2019 |
| Variational annealing of GANs: A Langevin perspective C Tao, S Dai, L Chen, K Bai, J Chen, C Liu, R Zhang, G Bobashev, ... International conference on machine learning, 6176-6185, 2019 | 22 | 2019 |
Chenyang TaoApplied Scientist, Amazon; Former Senior Research Associate, Duke University (Statistics and ECE)在 duke.edu 的电子邮件经过验证
