1 code implementation • 7 Sep 2023 • Ruigang Zheng, Xiaosheng Zhuang
In this paper, we propose a novel and general framework to construct tight framelet systems on graphs with localized supports based on hierarchical partitions.
1 code implementation • 7 Jun 2023 • Jianfei Li, Ruigang Zheng, Han Feng, Ming Li, Xiaosheng Zhuang
The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network models and suggests aggregations beyond the 1-hop neighborhood.
no code implementations • 17 Jan 2022 • Jianfei Li, Han Feng, Xiaosheng Zhuang
In this paper, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition.
1 code implementation • 12 Dec 2020 • Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang
Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis.
no code implementations • 27 Aug 2020 • Yuchen Xiao, Xiaosheng Zhuang
Based on hierarchical partitions, we provide the construction of Haar-type tight framelets on any compact set $K\subseteq \mathbb{R}^d$.
no code implementations • 10 Oct 2019 • Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, Carola-Bibiane Schönlieb
We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.
no code implementations • 25 Sep 2019 • Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, Yanan Fan
The input of each pooling layer is transformed by the compressive Haar basis of the corresponding clustering.
1 code implementation • ICML 2020 • Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, Yanan Fan
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks.
no code implementations • 10 Jul 2019 • Ming Li, Zheng Ma, Yu Guang Wang, Xiaosheng Zhuang
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data.