activity
20192021
most citedHybrid Tensor Decomposition in Neural Network Compression

46 citations · 118 across the 5 of their papers we have counts for

collaborators

5 papers

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.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.CV2020★ 28 cited

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…

cs.CV2020★ 46 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.CV2019★ 26 cited

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…