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Found 8 papers, 4 papers with code
Embedding Graphs on Grassmann Manifold
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction.
Quasi-Framelets: Another Improvement to GraphNeural Networks
This paper aims to provide a novel design of a multiscale framelets convolution for spectral graph neural networks.
Graph Denoising with Framelet Regularizer
As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise.
Grassmann Graph Embedding
Geometric deep learning that employs the geometric and topological features of data has attracted increasing attention in deep neural networks.
How Framelets Enhance Graph Neural Networks
The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks.
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.
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
In this paper, we propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies.
On the Trend-corrected Variant of Adaptive Stochastic Optimization Methods
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.
