420 citations · 1.2k across the 58 of their papers we have counts for
11 papers · 1 filter
Dual adaptive training of photonic neural networks
Ziyang Zheng, Zhengyang Duan, Hang Chen +5
Photonic neural network (PNN) is a remarkable analog artificial intelligence (AI) accelerator that computes with photons instead of electrons to feature low latency, high energy ef…
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning
Mingxing Xu, Wenrui Dai, Chenglin Li +2
Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly co…
Graph Neural Networks With Lifting-based Adaptive Graph Wavelets
Mingxing Xu, Wenrui Dai, Chenglin Li +3
Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph f…
Message Passing in Graph Convolution Networks via Adaptive Filter Banks
Xing Gao, Wenrui Dai, Chenglin Li +3
Graph convolution networks, like message passing graph convolution networks (MPGCNs), have been a powerful tool in representation learning of networked data. However, when data is…
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification
Rui Yang, Wenrui Dai, Chenglin Li +2
Message passing has evolved as an effective tool for designing Graph Neural Networks (GNNs). However, most existing methods for message passing simply sum or average all the neighb…
MimicNorm: Weight Mean and Last BN Layer Mimic the Dynamic of Batch Normalization
Wen Fei, Wenrui Dai, Chenglin Li +2
Substantial experiments have validated the success of Batch Normalization (BN) Layer in benefiting convergence and generalization. However, BN requires extra memory and float-point…
Graph Pooling with Node Proximity for Hierarchical Representation Learning
Xing Gao, Wenrui Dai, Chenglin Li +2
Graph neural networks have attracted wide attentions to enable representation learning of graph data in recent works. In complement to graph convolution operators, graph pooling is…
TRP: Trained Rank Pruning for Efficient Deep Neural Networks
Yuhui Xu, Yuxi Li, Shuai Zhang +6
To enable DNNs on edge devices like mobile phones, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several…
iPool -- Information-based Pooling in Hierarchical Graph Neural Networks
Xing Gao, Hongkai Xiong, Pascal Frossard
With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural…
DNQ: Dynamic Network Quantization
Yuhui Xu, Shuai Zhang, Yingyong Qi +3
Network quantization is an effective method for the deployment of neural networks on memory and energy constrained mobile devices. In this paper, we propose a Dynamic Network Quant…
Deep Neural Network Compression with Single and Multiple Level Quantization
Yuhui Xu, Yongzhuang Wang, Aojun Zhou +2
Network quantization is an effective solution to compress deep neural networks for practical usage. Existing network quantization methods cannot sufficiently exploit the depth info…