| Learning intents behind interactions with knowledge graph for recommendation X Wang, T Huang, D Wang, Y Yuan, Z Liu, X He, TS Chua Proceedings of the web conference 2021, 878-887, 2021 | 477 | 2021 |
| Mixgcf: An improved training method for graph neural network-based recommender systems T Huang, Y Dong, M Ding, Z Yang, W Feng, X Wang, J Tang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 207 | 2021 |
| Grand+: Scalable graph random neural networks W Feng, Y Dong, T Huang, Z Yin, X Cheng, E Kharlamov, J Tang Proceedings of the ACM Web Conference 2022, 3248-3258, 2022 | 42 | 2022 |
| Does Negative Sampling Matter? A Review with Insights into its Theory and Applications Z Yang, M Ding, T Huang, Y Cen, J Song, B Xu, Y Dong, J Tang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 2024 | 13 | 2024 |
| BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs Z Yang, T Huang, M Ding, Y Dong, R Ying, Y Cen, Y Geng, J Tang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2023 | 8 | 2023 |
| Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer T Huang, Z Song, R Ying, W Jin Neural Information Processing Systems (NeurIPS), 2024 | 4 | 2024 |
| SurfPro: Functional Protein Design Based on Continuous Surface Z Song, T Huang, L Li, W Jin International Conference on Machine Learning (ICML), 2024 | 3 | 2024 |
| Neural Network-Based Deep Encoding for Mixed-Attribute Data Classification T Huang, Y He, D Dai, W Wang, JZ Huang Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2019 …, 2019 | 2 | 2019 |
| Learning to Group Auxiliary Datasets for Molecule T Huang, Z Hu, R Ying Neural Information Processing Systems (NeurIPS), 2023 | 1 | 2023 |
| Learning affective features based on vip for video affective content analysis Y Zhu, M Tong, T Huang, Z Wen, Q Tian Advances in Multimedia Information Processing–PCM 2018: 19th Pacific-Rim …, 2018 | 1 | 2018 |
| UNICORN: Towards Universal Cellular Expression Prediction with an Explainable Multi-Task Learning Framework H Zhao, T Liu, T Huang, Y Lin, R Ying | | 2025 |
| UNICORN: Towards Universal Cellular Expression Prediction with an Explainable Multi-Task Learning Framework T Liu, T Huang, Y Lin, R Ying, H Zhao bioRxiv, 2025.01. 22.634371, 2025 | | 2025 |
| spEMO: Exploring the Capacity of Foundation Models for Analyzing Spatial Multi-Omic Data T Liu, T Huang, R Ying, H Zhao bioRxiv, 2025.01. 13.632818, 2025 | | 2025 |
| HEART: Learning better representation of EHR data with a heterogeneous relation-aware transformer T Huang, SA Rizvi, RK Thakur, V Socrates, M Gupta, D van Dijk, ... Journal of Biomedical Informatics 159, 104741, 2024 | | 2024 |
| FAFormer: Frame Averaging Transformer for Predicting Nucleic Acid-Protein Interactions T Huang, Z Song, R Ying, W Jin Machine Learning for Structural Biology Workshop, NeurIPS 2023, 2023 | | 2023 |
| ProSampler: Improving Contrastive Learning by Better Mini-batch Sampling Z Yang, T Huang, M Ding, Z Ying, Y Cen, Y Geng, Y Dong, J Tang | | |
Tang JieWeBank Chair Professor, Tsinghua UniversityAdresse e-mail validée de tsinghua.edu.cn