15 citations · 32 across the 3 of their papers we have counts for
8 papers
Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks
Yifan Hu, Yujie Wu, Lei Deng +1
Despite the rapid progress of neuromorphic computing, the inadequate depth and the resulting insufficient representation power of spiking neural networks (SNNs) severely restrict t…
Going Deeper With Directly-Trained Larger Spiking Neural Networks
Hanle Zheng, Yujie Wu, Lei Deng +2
Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient…
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Weihua He, YuJie Wu, Lei Deng +6
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Sp…
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Ling Liang, Xing Hu, Lei Deng +5
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models ac…
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Lei Deng, Yujie Wu, Yifan Hu +6
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with h…
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking
Zheyu Yang, Yujie Wu, Guanrui Wang +5
Computer-science-oriented artificial neural networks (ANNs) have achieved tremendous success in a variety of scenarios via powerful feature extraction and high-precision data opera…