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Chun-Yin Huang
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2020 – today
- 2024
- [c5]Chun-Yin Huang, Kartik Srinivas, Xin Zhang, Xiaoxiao Li:
Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors. ICML 2024 - [c4]Ruinan Jin, Chun-Yin Huang, Chenyu You, Xiaoxiao Li:
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP. SaTML 2024: 272-285 - [i5]Ruinan Jin, Chun-Yin Huang, Chenyu You, Xiaoxiao Li:
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP. CoRR abs/2401.01911 (2024) - [i4]Chun-Yin Huang, Kartik Srinivas, Xin Zhang, Xiaoxiao Li:
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors. CoRR abs/2405.11525 (2024) - 2023
- [j1]Yangsibo Huang, Chun-Yin Huang, Xiaoxiao Li, Kai Li:
A Dataset Auditing Method for Collaboratively Trained Machine Learning Models. IEEE Trans. Medical Imaging 42(7): 2081-2090 (2023) - [i3]Chun-Yin Huang, Ruinan Jin, Can Zhao, Daguang Xu, Xiaoxiao Li:
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation. CoRR abs/2303.02278 (2023) - [i2]Konstantin D. Pandl, Chun-Yin Huang, Ivan Beschastnikh, Xiaoxiao Li, Scott Thiebes, Ali Sunyaev:
Scalable Data Point Valuation in Decentralized Learning. CoRR abs/2305.01657 (2023) - 2022
- [c3]Garima Aggarwal, Chun-Yin Huang, Di Fan, Xiaoxiao Li, Zehua Wang:
DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain. DeCaF/FAIR@MICCAI 2022: 100-109 - [c2]Chun-Yin Huang, Qi Lei, Xiaoxiao Li:
Efficient Medical Image Assessment via Self-supervised Learning. DALI@MICCAI 2022: 102-111 - [i1]Chun-Yin Huang, Qi Lei, Xiaoxiao Li:
Efficient Medical Image Assessment via Self-supervised Learning. CoRR abs/2209.14434 (2022) - 2021
- [c1]Chun-Yin Huang, John M. Galeotti:
Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance. ICRA 2021: 10974-10980
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last updated on 2024-09-04 00:22 CEST by the dblp team
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