no code implementations • 5 Feb 2024 • Siyuan Dai, Kai Ye, Kun Zhao, Ge Cui, Haoteng Tang, Liang Zhan
In this work, we introduce a novel approach predicated on representation distance-based mutual information (MI) maximization for measuring the significance of different views, aiming at conducting more efficient contrastive learning and representation disentanglement.
no code implementations • 14 Jul 2022 • Haoteng Tang, Guixiang Ma, Lei Guo, Xiyao Fu, Heng Huang, Liang Zhang
Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks.
no code implementations • 6 May 2022 • Haoteng Tang, Xiyao Fu, Lei Guo, Yalin Wang, Scott Mackin, Olusola Ajilore, Alex Leow, Paul Thompson, Heng Huang, Liang Zhan
Since brain networks derived from functional and structural MRI describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks is non-trivial.
no code implementations • 9 Aug 2021 • Haozhe Jia, Haoteng Tang, Guixiang Ma, Weidong Cai, Heng Huang, Liang Zhan, Yong Xia
In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel.
no code implementations • 9 Aug 2021 • Haoteng Tang, Haozhe Jia, Weidong Cai, Heng Huang, Yong Xia, Liang Zhan
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation.
no code implementations • 10 Dec 2020 • Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang, Liang Zhan
In this paper, we propose a new interpretable graph pooling framework - CommPOOL, that can capture and preserve the hierarchical community structure of graphs in the graph representation learning process.
no code implementations • 23 May 2020 • Haoteng Tang, Guixiang Ma, Yurong Chen, Lei Guo, Wei Wang, Bo Zeng, Liang Zhan
However, most of the existing work in this area focus on the GNNs for node-level tasks, while little work has been done to study the robustness of the GNNs for the graph classification task.