1.2k citations · 2.4k across the 41 of their papers we have counts for
9 papers · 1 filter
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…
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin +3
Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Thanks to the bio-p…
Restoring Negative Information in Few-Shot Object Detection
Yukuan Yang, Fangyun Wei, Miaojing Shi +1
Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of…
Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs
Dingheng Wang, Bijiao Wu, Guangshe Zhao +5
Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition. However, since the modern RNNs, i…
Hybrid Tensor Decomposition in Neural Network Compression
Bijiao Wu, Dingheng Wang, Guangshe Zhao +2
Deep neural networks (DNNs) have enabled impressive breakthroughs in various artificial intelligence (AI) applications recently due to its capability of learning high-level feature…
Brain-inspired global-local learning incorporated with neuromorphic computing
Yujie Wu, Rong Zhao, Jun Zhu +11
Two main routes of learning methods exist at present including error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide…
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…
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks
Zhaodong Chen, Lei Deng, Bangyan Wang +2
In recent years, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components an…