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Found 5 papers, 2 papers with code
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.
SignReLU neural network and its approximation ability
Deep neural networks (DNNs) have garnered significant attention in various fields of science and technology in recent years.
Approximation analysis of CNNs from a feature extraction view
In this paper we establish some analysis for linear feature extraction by a deep multi-channel convolutional neural networks (CNNs), which demonstrates the power of deep learning over traditional linear transformations, like Fourier, wavelets, redundant dictionary coding methods.
Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks
Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging.
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.
