activity
20172023
most citedHybrid Tensor Decomposition in Neural Network Compression

46 citations · 193 across the 17 of their papers we have counts for

collaborators
Showing 2021Show all

9 papers · 1 filter

cs.NE2021★ 15 cited

Advancing Spiking Neural Networks towards Deep Residual Learning

Yifan Hu, Lei Deng, Yujie Wu +2

Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their applicatio…

cs.CV2021★ 33 cited

ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks

Yihan Lin, Wei Ding, Shaohua Qiang +2

With event-driven algorithms, especially the spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream-dat…

cs.CV2021★ 18 cited

Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions

Yang Wu, Dingheng Wang, Xiaotong Lu +4

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence.…

cs.NE2021★ 2 cited

H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks

Ling Liang, Zheng Qu, Zhaodong Chen +6

Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits thei…

cs.CV2021

Temporal-wise Attention Spiking Neural Networks for Event Streams Classification

Man Yao, Huanhuan Gao, Guangshe Zhao +4

How to effectively and efficiently deal with spatio-temporal event streams, where the events are generally sparse and non-uniform and have the microsecond temporal resolution, is o…

cs.NE2021★ 4 cited

Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

Mingkun Xu, Yujie Wu, Lei Deng +3

Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex envi…