| Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms D Shen, G Wang, W Wang, MR Min, Q Su, Y Zhang, C Li, R Henao, ... ACL, 2018 | 182 | 2018 |
| Joint Embedding of Words and Labels for Text Classification G Wang, C Li, W Wang, Y Zhang, D Shen, X Zhang, R Henao, L Carin arXiv preprint arXiv:1805.04174, 2018 | 154 | 2018 |
| Deconvolutional paragraph representation learning Y Zhang, D Shen, G Wang, Z Gan, R Henao, L Carin Advances in Neural Information Processing Systems, 4169-4179, 2017 | 77 | 2017 |
| Adversarial Text Generation via Feature-Mover's Distance L Chen, S Dai, C Tao, H Zhang, Z Gan, D Shen, Y Zhang, G Wang, ... Advances in Neural Information Processing Systems, 4666-4677, 2018 | 70 | 2018 |
| Topic-Guided Variational Autoencoders for Text Generation W Wang, Z Gan, H Xu, R Zhang, G Wang, D Shen, C Chen, L Carin arXiv preprint arXiv:1903.07137, 2019 | 54 | 2019 |
| NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing D Shen, Q Su, P Chapfuwa, W Wang, G Wang, L Carin, R Henao arXiv preprint arXiv:1805.05361, 2018 | 33 | 2018 |
| Jointgan: Multi-domain joint distribution learning with generative adversarial nets Y Pu, S Dai, Z Gan, W Wang, G Wang, Y Zhang, R Henao, LC Duke International Conference on Machine Learning, 4151-4160, 2018 | 26 | 2018 |
| Generative Adversarial Network Training is a Continual Learning Problem KJ Liang, C Li, G Wang, L Carin arXiv preprint arXiv:1811.11083, 2018 | 20 | 2018 |
| An end-to-end generative architecture for paraphrase generation Q Yang, D Shen, Y Cheng, W Wang, G Wang, L Carin Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 18 | 2019 |
| 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, ... arXiv preprint arXiv:1906.01840, 2019 | 12 | 2019 |
| POINTER: Constrained Text Generation via Insertion-based Generative Pre-training Y Zhang, G Wang, C Li, Z Gan, C Brockett, B Dolan arXiv preprint arXiv:2005.00558, 2020 | 10 | 2020 |
| Adversarial learning of a sampler based on an unnormalized distribution C Li, K Bai, J Li, G Wang, C Chen, L Carin The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 8 | 2019 |
| Improving textual network learning with variational homophilic embeddings W Wang, C Tao, Z Gan, G Wang, L Chen, X Zhang, R Zhang, Q Yang, ... Advances in Neural Information Processing Systems, 2076-2087, 2019 | 7 | 2019 |
| Learning to Sample with Adversarially Learned Likelihood-Ratio C Li, J Li, G Wang, L Carin | 6 | 2018 |
| Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding D Sundararaman, V Subramanian, G Wang, S Si, D Shen, D Wang, ... arXiv preprint arXiv:1911.06156, 2019 | 4 | 2019 |
| Kernel-based approaches for sequence modeling: Connections to neural methods K Liang, G Wang, Y Li, R Henao, L Carin Advances in Neural Information Processing Systems, 3392-3403, 2019 | 4 | 2019 |
| Sequence Generation with Guider Network R Zhang, C Chen, Z Gan, W Wang, L Chen, D Shen, G Wang, L Carin arXiv preprint arXiv:1811.00696, 2018 | 4 | 2018 |
| Graph-driven generative models for heterogeneous multi-task learning W Wang, H Xu, Z Gan, B Li, G Wang, L Chen, Q Yang, W Wang, L Carin Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 979-988, 2020 | 3 | 2020 |
| Dual-scale Galerkin methods for Darcy flow G Wang, G Scovazzi, L Nouveau, CE Kees, S Rossi, O Colomés, A Main Journal of Computational Physics 354, 111-134, 2018 | 3 | 2018 |
| Improving Adversarial Text Generation by Modeling the Distant Future R Zhang, C Chen, Z Gan, W Wang, D Shen, G Wang, Z Wen, L Carin arXiv preprint arXiv:2005.01279, 2020 | 2 | 2020 |
