| 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 | 2905 | 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 | 1569 | 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 | 1473 | 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 | 588 | 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 | 413 | 2018 |
| 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 | 398 | 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 | 396 | 2017 |
| Improving lexical embeddings with semantic knowledge M Yu, M Dredze Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 384 | 2014 |
| One-shot relational learning for knowledge graphs W Xiong, M Yu, S Chang, X Guo, WY Wang arXiv preprint arXiv:1808.09040, 2018 | 319 | 2018 |
| 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 | 311 | 2018 |
| Factor-based compositional embedding models M Yu, M Gormley, M Dredze NIPS workshop on learning semantics 411, 95-101, 2014 | 304* | 2014 |
| 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 | 292 | 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 | 285 | 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 | 273 | 2018 |
| Invariant rationalization S Chang, Y Zhang, M Yu, T Jaakkola International Conference on Machine Learning, 1448-1458, 2020 | 254 | 2020 |
| Simple question answering by attentive convolutional neural network W Yin, M Yu, B Xiang, B Zhou, H Schütze arXiv preprint arXiv:1606.03391, 2016 | 243 | 2016 |
| 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 | 207 | 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 | 187 | 2019 |
| On the origin of hallucinations in conversational models: Is it the datasets or the models? N Dziri, S Milton, M Yu, O Zaiane, S Reddy arXiv preprint arXiv:2204.07931, 2022 | 184 | 2022 |
| Leveraging sentence-level information with encoder LSTM for natural language understanding G Kurata, B Xiang, B Zhou, M Yu arXiv preprint arXiv:1601.01530, 2016 | 180* | 2016 |
Shiyu ChangUniversity of California, Santa Barbara在 cs.ucsb.edu 的电子邮件经过验证