14 citations · 75 across the 18 of their papers we have counts for
6 papers · 1 filter
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Langzhang Liang, Cuiyun Gao, Shiyi Chen +5
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, l…
Fast query-by-example speech search using separable model
Yuguang Yang, Yu Pan, Xin Dong +1
Traditional Query-by-Example (QbE) speech search approaches usually use methods based on frame-level features, while state-of-the-art approaches tend to use models based on acousti…
Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations
Yiwei Chen, Yu Pan, Daoyi Dong
States of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modelled. In machine learning, comp…
AFINet: Attentive Feature Integration Networks for Image Classification
Xinglin Pan, Jing Xu, Yu Pan +4
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets,…
TedNet: A Pytorch Toolkit for Tensor Decomposition Networks
Yu Pan, Maolin Wang, Zenglin Xu
Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named T…
RegNet: Self-Regulated Network for Image Classification
Jing Xu, Yu Pan, Xinglin Pan +3
The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple sho…