| Collaborative Variational Autoencoder for Recommender Systems X Li, J She ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017 | 531 | 2017 |
| Multi-lingual Evaluation of Code Generation Models B Athiwaratkun, SK Gouda, Z Wang, X Li, Y Tian, M Tan, WU Ahmad, ... ICLR, 2023 | 72 | 2023 |
| Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering X Li, Z Chen, LKM Poon, NL Zhang International Conference on Learning Representations, 2019 | 70 | 2019 |
| Learning to Abstract for Memory-augmented Conversational Response Generation Z Tian, W Bi, X Li, NL Zhang The 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019 | 46 | 2019 |
| Not All Attention is Needed: Gated Attention Network for Sequence Data L Xue, X Li, NL Zhang AAAI, 2020 | 25 | 2020 |
| Connection Discovery using Shared Images by Gaussian Relational Topic Model X Li, M Cheung, J She IEEE International Conference on Big Data, 2016 | 16 | 2016 |
| Exploring Continual Learning for Code Generation Models P Yadav, Q Sun, H Ding, X Li, D Zhang, M Tang, X Ma, P Bhatia, ... ACL, 2023 | 15 | 2023 |
| ContraCLM: Contrastive Learning For Causal Language Model N Jain, D Zhang, WU Ahmad, Z Wang, F Nan, X Li, M Tan, R Nallapati, ... ACL, 2023 | 5 | 2023 |
| Fast Structure Learning for Deep Feedforward Networks via Tree Skeleton Expansion Z Chen, X Li, Z Tian, NL Zhang Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th …, 2019 | 3* | 2019 |
| Relational Variational Autoencoder for Link Prediction with Multimedia Data X Li, J She ACM SIGMM International Conference on Multimedia Thematic Workshop, 93-100, 2017 | 3 | 2017 |
| Building Sparse Deep Feedforward Networks using Tree Receptive Fields X Li, Z Chen, NL Zhang International Joint Conference on Artificial Intelligence, 2018 | 1 | 2018 |
| Training LLMs to Better Self-Debug and Explain Code N Jiang, X Li, S Wang, Q Zhou, SB Hossain, B Ray, V Kumar, ... arXiv, 2024 | | 2024 |
