8 citations · 11 across the 8 of their papers we have counts for
7 papers · 1 filter
Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture
Shijin Duan, Yejia Liu, Gaowen Liu +3
Vector Symbolic Architecture (VSA) is emerging in machine learning due to its efficiency, but they are hindered by issues of hyperdimensionality and accuracy. As a promising mitiga…
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
Tong Zhou, Jiahui Zhao, Yukui Luo +4
Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, exi…
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces
Yejia Liu, Shijin Duan, Xiaolin Xu +1
Brain-Computer interfaces (BCIs) are typically designed to be lightweight and responsive in real-time to provide users timely feedback. Classical feature engineering is computation…
LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
Hongwu Peng, Ran Ran, Yukui Luo +8
The growth of Graph Convolution Network (GCN) model sizes has revolutionized numerous applications, surpassing human performance in areas such as personal healthcare and financial…
VertexSerum: Poisoning Graph Neural Networks for Link Inference
Ruyi Ding, Shijin Duan, Xiaolin Xu +1
Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links,…
LeHDC: Learning-Based Hyperdimensional Computing Classifier
Shijin Duan, Yejia Liu, Shaolei Ren +1
Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless,…