| A structured self-attentive sentence embedding Z Lin, M Feng, CN Santos, M Yu, B Xiang, B Zhou, Y Bengio arXiv preprint arXiv:1703.03130, 2017 | 2798 | 2017 |
| Comparative study of CNN and RNN for natural language processing W Yin, K Kann, M Yu, H Schütze arXiv preprint arXiv:1702.01923, 2017 | 1447 | 2017 |
| Target-dependent twitter sentiment classification L Jiang, M Yu, M Zhou, X Liu, T Zhao Proceedings of the 49th annual meeting of the association for computational …, 2011 | 1439 | 2011 |
| DAG-GNN: DAG structure learning with graph neural networks Y Yu, J Chen, T Gao, M Yu International conference on machine learning, 7154-7163, 2019 | 502 | 2019 |
| R3: Reinforced Ranker-Reader for Open-Domain Question Answering S Wang, M Yu, X Guo, Z Wang, T Klinger, W Zhang, S Chang, G Tesauro, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 395 | 2018 |
| Improving lexical embeddings with semantic knowledge M Yu, M Dredze Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 383 | 2014 |
| Improved neural relation detection for knowledge base question answering M Yu, W Yin, KS Hasan, C Santos, B Xiang, B Zhou arXiv preprint arXiv:1704.06194, 2017 | 382 | 2017 |
| Dilated recurrent neural networks S Chang, Y Zhang, W Han, M Yu, X Guo, W Tan, X Cui, M Witbrock, ... Advances in neural information processing systems 30, 2017 | 371 | 2017 |
| Diverse few-shot text classification with multiple metrics M Yu, X Guo, J Yi, S Chang, S Potdar, Y Cheng, G Tesauro, H Wang, ... arXiv preprint arXiv:1805.07513, 2018 | 302 | 2018 |
| Factor-based compositional embedding models M Yu, M Gormley, M Dredze NIPS workshop on learning semantics 411, 95-101, 2014 | 290* | 2014 |
| One-shot relational learning for knowledge graphs W Xiong, M Yu, S Chang, X Guo, WY Wang arXiv preprint arXiv:1808.09040, 2018 | 286 | 2018 |
| Cross-lingual knowledge graph alignment via graph matching neural network K Xu, L Wang, M Yu, Y Feng, Y Song, Z Wang, D Yu arXiv preprint arXiv:1905.11605, 2019 | 271 | 2019 |
| Differential treatment for stuff and things: A simple unsupervised domain adaptation method for semantic segmentation Z Wang, M Yu, Y Wei, R Feris, J Xiong, W Hwu, TS Huang, H Shi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 270 | 2020 |
| Image super-resolution via dual-state recurrent networks W Han, S Chang, D Liu, M Yu, M Witbrock, TS Huang Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 261 | 2018 |
| Simple question answering by attentive convolutional neural network W Yin, M Yu, B Xiang, B Zhou, H Schütze arXiv preprint arXiv:1606.03391, 2016 | 236 | 2016 |
| Invariant rationalization S Chang, Y Zhang, M Yu, T Jaakkola International Conference on Machine Learning, 1448-1458, 2020 | 221 | 2020 |
| Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering S Wang, M Yu, J Jiang, W Zhang, X Guo, S Chang, Z Wang, T Klinger, ... International Conference on Learning Representations (ICLR), 2018 | 204 | 2018 |
| Improving natural language inference using external knowledge in the science questions domain X Wang, P Kapanipathi, R Musa, M Yu, K Talamadupula, I Abdelaziz, ... Proceedings of the AAAI conference on artificial intelligence 33 (01), 7208-7215, 2019 | 174 | 2019 |
| Leveraging sentencelevel information with encoder lstm for natural language understanding G Kurata, B Xiang, B Zhou, M Yu arXiv preprint arXiv:1601.01530, 2016 | 173* | 2016 |
| Rethinking cooperative rationalization: Introspective extraction and complement control M Yu, S Chang, Y Zhang, TS Jaakkola arXiv preprint arXiv:1910.13294, 2019 | 155 | 2019 |
Shiyu ChangUniversity of California, Santa Barbara在 cs.ucsb.edu 的电子邮件经过验证
