activity
20192022
most citedDefending Neural Backdoors via Generative Distribution Modeling

65 citations · 76 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NE2022

A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation

Yukun Yang, Peng Li

Several recent studies attempt to address the biological implausibility of the well-known backpropagation (BP) method. While promising methods such as feedback alignment, direct fe…

cs.NE20213 cited

Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks

Yukun Yang, Wenrui Zhang, Peng Li

While backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a continuous-valued loss o…

cs.NE2020

Temporal Surrogate Back-propagation for Spiking Neural Networks

Yukun Yang

Spiking neural networks (SNN) are usually more energy-efficient as compared to Artificial neural networks (ANN), and the way they work has a great similarity with our brain. Back-p…

cs.LG201965 cited

Defending Neural Backdoors via Generative Distribution Modeling

Ximing Qiao, Yukun Yang, Hai Li

Neural backdoor attack is emerging as a severe security threat to deep learning, while the capability of existing defense methods is limited, especially for complex backdoor trigge…

cs.LG20198 cited

SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures

Hsin-Pai Cheng, Tunhou Zhang, Yukun Yang +5

Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost. Traditionally, researchers manually craft deep neu…