| Improving automatic source code summarization via deep reinforcement learning Y Wan, Z Zhao, M Yang, G Xu, H Ying, J Wu, PS Yu ASE 2018, 397-407, 2018 | 460 | 2018 |
| Kg-bart: Knowledge graph-augmented bart for generative commonsense reasoning Y Liu, Y Wan, L He, H Peng, SY Philip AAAI 2021 35 (7), 6418-6425, 2021 | 219 | 2021 |
| Multi-modal attention network learning for semantic source code retrieval Y Wan, J Shu, Y Sui, G Xu, Z Zhao, J Wu, PS Yu ASE 2019, 2019 | 195 | 2019 |
| Find or classify? dual strategy for slot-value predictions on multi-domain dialog state tracking JG Zhang, K Hashimoto, CS Wu, Y Wan, PS Yu, R Socher, C Xiong *SEM 2020, 2019 | 190 | 2019 |
| What Do They Capture?--A Structural Analysis of Pre-Trained Language Models for Source Code Y Wan, W Zhao, H Zhang, Y Sui, G Xu, H Jin ICSE 2022, 2022 | 129 | 2022 |
| Syncobert: Syntax-guided multi-modal contrastive pre-training for code representation X Wang, Y Wang, F Mi, P Zhou, Y Wan, X Liu, L Li, H Wu, J Liu, X Jiang arXiv preprint arXiv:2108.04556, 2021 | 123 | 2021 |
| Reinforcement-learning-guided source code summarization using hierarchical attention W Wang, Y Zhang, Y Sui, Y Wan, Z Zhao, J Wu, SY Philip, G Xu IEEE Transactions on software Engineering 48 (1), 102-119, 2020 | 123 | 2020 |
| FedBERT: When Federated Learning Meets Pre-training Y Tian, Y Wan, L Lyu, D Yao, H Jin, L Sun ACM Transactions on Intelligent Systems and Technology (TIST) 13 (4), 1-26, 2022 | 121 | 2022 |
| FCCA: Hybrid code representation for functional clone detection using attention networks W Hua, Y Sui, Y Wan, G Liu, G Xu IEEE Transactions on Reliability 70 (1), 304-318, 2020 | 103 | 2020 |
| Discriminative nearest neighbor few-shot intent detection by transferring natural language inference JG Zhang, K Hashimoto, W Liu, CS Wu, Y Wan, PS Yu, R Socher, C Xiong EMNLP 2020, 2020 | 91 | 2020 |
| Metatool benchmark for large language models: Deciding whether to use tools and which to use Y Huang, J Shi, Y Li, C Fan, S Wu, Q Zhang, Y Liu, P Zhou, Y Wan, ... ICLR 2024, 2023 | 68 | 2023 |
| Compilable Neural Code Generation with Compiler Feedback X Wang, Y Wang, Y Wan, F Mi, Y Li, P Zhou, J Liu, H Wu, X Jiang, Q Liu ACL 2022 (Findings), 2022 | 68 | 2022 |
| DANets: Deep Abstract Networks for Tabular Data Classification and Regression J Chen, K Liao, Y Wan, DZ Chen, J Wu AAAI 2022, 2021 | 63 | 2021 |
| You see what I want you to see: poisoning vulnerabilities in neural code search Y Wan, S Zhang, H Zhang, Y Sui, G Xu, D Yao, H Jin, L Sun ESEC/FSE 2022, 1233-1245, 2022 | 61 | 2022 |
| Local-global knowledge distillation in heterogeneous federated learning with non-iid data D Yao, W Pan, Y Dai, Y Wan, X Ding, H Jin, Z Xu, L Sun arXiv preprint arXiv:2107.00051, 2021 | 59 | 2021 |
| MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark D Chen, R Chen, S Zhang, Y Liu, Y Wang, H Zhou, Q Zhang, Y Wan, ... ICML 2024 (Oral), 2024 | 52 | 2024 |
| Multi-triage: A multi-task learning framework for bug triage TWW Aung, Y Wan, H Huo, Y Sui Journal of Systems and Software 184, 111133, 2022 | 43 | 2022 |
| Fedhm: Efficient federated learning for heterogeneous models via low-rank factorization D Yao, W Pan, MJ O'Neill, Y Dai, Y Wan, H Jin, L Sun arXiv preprint arXiv:2111.14655, 2021 | 43 | 2021 |
| Are pretrained transformers robust in intent classification? a missing ingredient in evaluation of out-of-scope intent detection JG Zhang, K Hashimoto, Y Wan, Y Liu, C Xiong, PS Yu Proceedings of the 4th Workshop on NLP for Conversational AI, ACL 2022, 2021 | 38 | 2021 |
| CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training X Wang, Y Wang, Y Wan, J Wang, P Zhou, L Li, H Wu, J Liu NAACL 2022 (Findings), 2022 | 35 | 2022 |
Philip S. YuProfessor of Computer Science, University of Illinons at ChicagoZweryfikowany adres z cs.uic.edu