Tinglin Huang
Tinglin Huang
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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
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
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
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
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
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
Learning to Group Auxiliary Datasets for Molecule
T Huang, Z Hu, R Ying
Neural Information Processing Systems (NeurIPS), 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
Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer
T Huang, Z Song, R Ying, W Jin
arXiv preprint arXiv:2406.09586, 2024
SurfPro: Functional Protein Design Based on Continuous Surface
Z Song, T Huang, L Li, W Jin
International Conference on Machine Learning (ICML), 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
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
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