| What Makes Good In-Context Examples for GPT-? J Liu, D Shen, Y Zhang, B Dolan, L Carin, W Chen arXiv preprint arXiv:2101.06804, 2021 | 1175 | 2021 |
| Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation X Wang, Q Huang, A Celikyilmaz, J Gao, D Shen, YF Wang, WY Wang, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 593 | 2019 |
| 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 | 535 | 2018 |
| Adversarial feature matching for text generation Y Zhang, Z Gan, K Fan, Z Chen, R Henao, D Shen, L Carin International conference on machine learning, 4006-4015, 2017 | 470 | 2017 |
| 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, ... arXiv preprint arXiv:1805.09843, 2018 | 450 | 2018 |
| Video generation from text Y Li, M Min, D Shen, D Carlson, L Carin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 282 | 2018 |
| Understanding the solvent-assisted crystallization mechanism inherent in efficient organic–inorganic halide perovskite solar cells D Shen, X Yu, X Cai, M Peng, Y Ma, X Su, L Xiao, D Zou Journal of Materials Chemistry A 2 (48), 20454-20461, 2014 | 179 | 2014 |
| 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 31, 2018 | 169 | 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 | 156 | 2019 |
| A simple but tough-to-beat data augmentation approach for natural language understanding and generation D Shen, M Zheng, Y Shen, Y Qu, W Chen arXiv preprint arXiv:2009.13818, 2020 | 144 | 2020 |
| Deconvolutional paragraph representation learning Y Zhang, D Shen, G Wang, Z Gan, R Henao, L Carin Advances in Neural Information Processing Systems 30, 2017 | 119 | 2017 |
| Improving disentangled text representation learning with information-theoretic guidance P Cheng, MR Min, D Shen, C Malon, Y Zhang, Y Li, L Carin arXiv preprint arXiv:2006.00693, 2020 | 105 | 2020 |
| 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, ... arXiv preprint arXiv:1901.06283, 2019 | 102 | 2019 |
| Coda: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding Y Qu, D Shen, Y Shen, S Sajeev, J Han, W Chen arXiv preprint arXiv:2010.08670, 2020 | 93 | 2020 |
| Topic compositional neural language model W Wang, Z Gan, W Wang, D Shen, J Huang, W Ping, S Satheesh, L Carin International Conference on Artificial Intelligence and Statistics, 356-365, 2018 | 92 | 2018 |
| Deconvolutional latent-variable model for text sequence matching D Shen, Y Zhang, R Henao, Q Su, L Carin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 88 | 2018 |
| Mixkd: Towards efficient distillation of large-scale language models KJ Liang, W Hao, D Shen, Y Zhou, W Chen, C Chen, L Carin arXiv preprint arXiv:2011.00593, 2020 | 73 | 2020 |
| 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 | 68 | 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 arXiv preprint arXiv:1902.00154, 2019 | 67 | 2019 |
| Syntax-infused variational autoencoder for text generation X Zhang, Y Yang, S Yuan, D Shen, L Carin arXiv preprint arXiv:1906.02181, 2019 | 64 | 2019 |
Lawrence CarinDuke UniversityVerifierad e-postadress på duke.edu