2 citations · 5 across the 10 of their papers we have counts for
7 papers
ROG: Robust Open-Set Graph Learning via Region-Based Prototype Learning
Qin Zhang, Xiaowei Li, Jiexin Lu +4
Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods…
SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks
Zizhen Liu, Weiyang He, Chip-Hong Chang +3
While Federated learning (FL) is attractive for pulling privacy-preserving distributed training data, the credibility of participating clients and non-inspectable data pose new sec…
DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs
Erjing Luo, Haitong Huang, Cheng Liu +5
Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computi…
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance
Xinghua Xue, Cheng Liu, Bo Liu +6
Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought…
DHSA: Efficient Doubly Homomorphic Secure Aggregation for Cross-silo Federated Learning
Zizhen Liu, Si Chen, Jing Ye +3
Secure aggregation is widely used in horizontal Federated Learning (FL), to prevent leakage of training data when model updates from data owners are aggregated. Secure aggregation…
Taming Process Variations in CNFET for Efficient Last Level Cache Design
Dawen Xu, Zhuangyu Feng, Cheng Liu +5
Carbon nanotube field-effect transistors (CNFET) emerge as a promising alternative to CMOS transistors for the much higher speed and energy efficiency, which makes the technology p…