5 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
Jinglong Luo, Guanzhong Chen, Yehong Zhang +6
With the growing deployment of pre-trained models like Transformers on cloud platforms, privacy concerns about model parameters and inference data are intensifying. Existing Privac…
FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data
Yukun Zhang, Guanzhong Chen, Zenglin Xu +6
Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods c…
Can we only use guideline instead of shot in prompt?
Jiaxiang Chen, Song Wang, Zhucong Li +4
Currently, prompting techniques can be mainly divided into two categories:1)shot method implicitly inspires the model to answer the question by mimicing the steps in the given exam…
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Langzhang Liang, Sunwoo Kim, Kijung Shin +3
Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in hete…
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
Shiyi Qi, Zenglin Xu, Yiduo Li +4
Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising res…