| Learning deep representations for graph clustering. F Tian, B Gao, Q Cui, E Chen, TY Liu AAAI 2014, 1293-1299, 2014 | 559 | 2014 |
| Neural architecture optimization R Luo, F Tian, T Qin, TY Liu NIPS 2018, 2018 | 494 | 2018 |
| Achieving human parity on automatic chinese to english news translation H Hassan, A Aue, C Chen, V Chowdhary, J Clark, C Federmann, X Huang, ... arXiv preprint arXiv:1803.05567, 2018 | 491 | 2018 |
| Learning to Teach Y Fan, F Tian, T Qin, XY Li, TY Liu ICLR-2018, Proceedings of Sixth International Conference on Learning …, 2018 | 193* | 2018 |
| A probabilistic model for learning multi-prototype word embeddings F Tian, H Dai, J Bian, B Gao, R Zhang, E Chen, TY Liu Proceedings of COLING 2014, the 25th International Conference on …, 2014 | 165 | 2014 |
| Deliberation networks: Sequence generation beyond one-pass decoding Y Xia, F Tian, L Wu, J Lin, T Qin, TY Liu Advances in Neural Information Processing Systems (NIPS), 1782-1792, 2017 | 158 | 2017 |
| Word embedding revisited: A new representation learning and explicit matrix factorization perspective Y Li, L Xu, F Tian, L Jiang, X Zhong, E Chen Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 155 | 2015 |
| A Study of Reinforcement Learning for Neural Machine Translation L Wu, F Tian, T Qin, J Lai, TY Liu EMNLP 2018, 2018 | 124 | 2018 |
| Adversarial neural machine translation L Wu, Y Xia, L Zhao, F Tian, T Qin, J Lai, TY Liu ACML2018, 2017 | 119 | 2017 |
| Non-Autoregressive Machine Translation with Auxiliary Regularization Y Wang, F Tian, D He, T Qin, CX Zhai, TY Liu AAAI, 2019 | 99 | 2019 |
| Recurrent Residual Learning for Sequence Classification Y Wang, F Tian EMNLP 2016, 938--943, 2016 | 97 | 2016 |
| Learning to Teach with Dynamic Loss Functions L Wu, F Tian, Y Xia, Y Fan, T Qin, J Lai, TY Liu NIPS 2018, 2018 | 74 | 2018 |
| Hint-based training for non-autoregressive machine translation Z Li, Z Lin, D He, F Tian, T Qin, L Wang, TY Liu arXiv preprint arXiv:1909.06708, 2019 | 59 | 2019 |
| Multi-agent dual learning Y Wang, Y Xia, T He, F Tian, T Qin, CX Zhai, TY Liu Proceedings of the International Conference on Learning Representations …, 2019 | 51 | 2019 |
| Model-level dual learning Y Xia, X Tan, F Tian, T Qin, N Yu, TY Liu International Conference on Machine Learning, 5383-5392, 2018 | 50 | 2018 |
| Towards Binary-Valued Gates for Robust LSTM Training Z Li, D He, F Tian, W Chen, T Qin, L Wang, TY Liu ICML 2018, 2018 | 47 | 2018 |
| Solving verbal comprehension questions in IQ test by knowledge-powered word embedding H Wang, F Tian, B Gao, J Bian, TY Liu EMNLP 2016, 541--550, 2016 | 35* | 2016 |
| Beyond error propagation in neural machine translation: Characteristics of language also matter L Wu, X Tan, D He, F Tian, T Qin, J Lai, TY Liu arXiv preprint arXiv:1809.00120, 2018 | 29 | 2018 |
| Sentence level recurrent topic model: letting topics speak for themselves F Tian, B Gao, D He, TY Liu arXiv preprint arXiv:1604.02038, 2016 | 25 | 2016 |
| Word Attention for Sequence to Sequence Text Understanding L Wu, F Tian, L Zhao, J Lai, TY Liu AAAI, 2018, 2018 | 21 | 2018 |
Tie-Yan LiuDistinguished Scientist, Microsoft | Assistant Managing Director, MSRA | IEEE Fellow | ACM FellowVerified email at microsoft.com
