| Unified language model pre-training for natural language understanding and generation L Dong, N Yang, W Wang, F Wei, X Liu, Y Wang, J Gao, M Zhou, HW Hon Advances in neural information processing systems 32, 2019 | 1208 | 2019 |
| Unilmv2: Pseudo-masked language models for unified language model pre-training H Bao, L Dong, F Wei, W Wang, N Yang, X Liu, Y Wang, J Gao, S Piao, ... International conference on machine learning, 642-652, 2020 | 277 | 2020 |
| A new concept using lstm neural networks for dynamic system identification Y Wang 2017 American control conference (ACC), 5324-5329, 2017 | 206 | 2017 |
| A bi-model based rnn semantic frame parsing model for intent detection and slot filling Y Wang, Y Shen, H Jin arXiv preprint arXiv:1812.10235, 2018 | 176 | 2018 |
| Adversarial training for large neural language models X Liu, H Cheng, P He, W Chen, Y Wang, H Poon, J Gao arXiv preprint arXiv:2004.08994, 2020 | 102 | 2020 |
| A neural transition-based model for nested mention recognition B Wang, W Lu, Y Wang, H Jin arXiv preprint arXiv:1810.01808, 2018 | 91 | 2018 |
| The microsoft toolkit of multi-task deep neural networks for natural language understanding X Liu, Y Wang, J Ji, H Cheng, X Zhu, E Awa, P He, W Chen, H Poon, ... arXiv preprint arXiv:2002.07972, 2020 | 43 | 2020 |
| Fast reinforcement learning using multiple models KS Narendra, Y Wang, S Mukhopadhay 2016 IEEE 55th Conference on Decision and Control (CDC), 7183-7188, 2016 | 33 | 2016 |
| Stability, robustness, and performance issues in second level adaptation KS Narendra, Y Wang, W Chen 2014 American Control Conference, 2377-2382, 2014 | 25 | 2014 |
| A deep reinforcement learning based multi-step coarse to fine question answering (mscqa) system Y Wang, H Jin Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 7224-7232, 2019 | 23 | 2019 |
| A boosting-based deep neural networks algorithm for reinforcement learning Y Wang, H Jin 2018 Annual American Control Conference (ACC), 1065-1071, 2018 | 20 | 2018 |
| A Deep Reinforcement Learning Based Multimodal Coaching Model (DCM) for Slot Filling in Spoken Language Understanding (SLU). Y Wang, A Patel, Y Shen, H Jin INTERSPEECH, 3444-3448, 2018 | 17 | 2018 |
| Design and implementation of an integrated multi-functional autonomous parking system with fuzzy logic controller Y Wang, X Zhu 2012 American Control Conference (ACC), 2928-2933, 2012 | 17 | 2012 |
| A new concept of deep reinforcement learning based augmented general tagging system Y Wang, A Patel, H Jin Proceedings of the 27th International Conference on Computational …, 2018 | 16 | 2018 |
| Hybrid fuzzy logic controller for optimized autonomous parking Y Wang, X Zhu 2013 American Control Conference, 182-187, 2013 | 14 | 2013 |
| Improving the speed of response of learning algorithms using multiple models KS Narendra, S Mukhopadyhay, Y Wang arXiv preprint arXiv:1510.05034, 2015 | 13 | 2015 |
| The rationale for second level adaptation KS Narendra, Y Wang, W Chen arXiv preprint arXiv:1510.04989, 2015 | 13 | 2015 |
| Extension of second level adaptation using multiple models to SISO systems KS Narendra, Y Wang, W Chen 2015 American Control Conference (ACC), 171-176, 2015 | 12 | 2015 |
| A robust design of hybrid fuzzy controller with fuzzy decision tree for autonomous intelligent parking system Y Wang, X Zhu 2014 American Control Conference, 5282-5287, 2014 | 12 | 2014 |
| System and method for personalized natural language understanding Y Shen, X Zeng, Y Wang, H Jin US Patent 11,094,317, 2021 | 10 | 2021 |
Hongxia JinSamsung Research AmericaVerified email at acm.org
