Xinyi Fu
This page is a disambiguation page, it actually contains multiple papers from persons of the same or a similar name.
Known people with the same name:
- Xinyi Fu 001 (Southwest Jiaotong University, Chengdu, China)
- Xinyi Fu 002 (First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China)
- Xinyi Fu 003 (Tsinghua University, Beijing, China)
Bibliography
2026
DongYuan: An LLM-Based Framework for Integrative Chinese and Western Medicine Spleen-Stomach Disorders Diagnosis.
CoRR, March, 2026
CoRR, March, 2026
Zero-shot Multi-Contrast Brain MRI Registration by Intensity Randomizing T1-weighted MRI (LUMIR25).
CoRR, February, 2026
IEEE Access, 2026
2025
I Can Tell Your Secrets: Inferring Privacy Attributes from Mini-app Interaction History in Super-apps.
CoRR, March, 2025
I Can Tell Your Secrets: Inferring Privacy Attributes from Mini-app Interaction History in Super-apps.
Proceedings of the 34th USENIX Security Symposium, 2025
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2, 2025
Proceedings of the IEEE International Symposium on Mixed and Augmented Reality, 2025
Proceedings of the 28th International Conference on Computer Supported Cooperative Work in Design, 2025
Proceedings of the Web and Big Data - 9th International Joint Conference, 2025
2024
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation.
CoRR, 2024
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
2023
Privacy-preserving design of graph neural networks with applications to vertical federated learning.
CoRR, 2023
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
Proceedings of the 30th IEEE/ACM International Symposium on Quality of Service, 2022
2020
Exploiting Adversarial Examples to Drain Computational Resources on Mobile Deep Learning Systems.
Proceedings of the 5th IEEE/ACM Symposium on Edge Computing, 2020