Search Results for author:
Found 9 papers, 4 papers with code
Data-Adaptive Graph Framelets with Generalized Vanishing Moments for Graph Signal Processing
In this paper, we propose a novel and general framework to construct tight framelet systems on graphs with localized supports based on hierarchical partitions.
Permutation Equivariant Graph Framelets for Heterophilous Graph Learning
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.
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets
In this paper, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition.
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis.
Adaptive directional Haar tight framelets on bounded domains for digraph signal representations
Based on hierarchical partitions, we provide the construction of Haar-type tight framelets on any compact set $K\subseteq \mathbb{R}^d$.
Dynamic Spectral Residual Superpixels
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.
HaarPooling: Graph Pooling with Compressive Haar Basis
The input of each pooling layer is transformed by the compressive Haar basis of the corresponding clustering.
Haar Graph Pooling
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks.
Fast Haar Transforms for Graph Neural Networks
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.
