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20182022
most citedHybrid Tensor Decomposition in Neural Network Compression

46 citations · 114 across the 16 of their papers we have counts for

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6 papers · 1 filter

cs.CV20221 cited

Attention Spiking Neural Networks

Man Yao, Guangshe Zhao, Hengyu Zhang +5

Benefiting from the event-driven and sparse spiking characteristics of the brain, spiking neural networks (SNNs) are becoming an energy-efficient alternative to artificial neural n…

cs.CV202046 cited

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…

cs.CV202015 cited

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…

cs.CV2019

Compressing 3DCNNs Based on Tensor Train Decomposition

Dingheng Wang, Guangshe Zhao, Guoqi Li +2

Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convol…

cs.CV2019

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…

cs.CV2018

Crossbar-aware neural network pruning

Ling Liang, Lei Deng, Yueling Zeng +5

Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operati…