1 code implementation • 30 May 2022 • Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction.
no code implementations • 11 Jan 2022 • Mengxi Yang, Xuebin Zheng, Jie Yin, Junbin Gao
This paper aims to provide a novel design of a multiscale framelets convolution for spectral graph neural networks.
1 code implementation • 5 Nov 2021 • Bingxin Zhou, Ruikun Li, Xuebin Zheng, Yu Guang Wang, Junbin Gao
As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise.
no code implementations • ICLR Workshop GTRL 2021 • Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao
Geometric deep learning that employs the geometric and topological features of data has attracted increasing attention in deep neural networks.
1 code implementation • 13 Feb 2021 • Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yu Guang Wang, Pietro Lio, Ming Li, Guido Montufar
The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks.
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 • 22 Jul 2020 • Xuebin Zheng, Bingxin Zhou, Ming Li, Yu Guang Wang, Junbin Gao
In this paper, we propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies.
no code implementations • 17 Jan 2020 • Bingxin Zhou, Xuebin Zheng, Junbin Gao
Adam-type optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning.