1.2k citations · 2k across the 39 of their papers we have counts for
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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…
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Xiaobing Chen, Yuke Wang, Xinfeng Xie +9
Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce,…
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…
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li +4
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the…
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…