A review on spectral data preprocessing techniques for machine learning ...
Jul 18, 2025 · The figure summarizes fundamental energy transitions (A), spectral classifications (B-C), instrumentation (D), spectral data structure and preprocessing (E and F), machine learning methods …
Kolmogorov–Arnold graph neural networks for molecular ... - Nature
Aug 11, 2025 · Graph neural networks (GNNs) have shown remarkable success in molecular property prediction as key models in geometric deep learning. Meanwhile, Kolmogorov–Arnold networks …
A Unified Framework for Structured Graph Learning via Spectral Constraints
We propose to convert combinatorial structural constraints into spectral constraints on graph matrices and develop an optimization framework based on block majorization-minimization to solve structured …
A Comprehensive Survey on Spectral Clustering with Graph Structure Learning
Abstract Spectral clustering is a powerful technique for clustering high-dimensional data, utilizing graph-based representations to detect complex, non-linear structures and non-convex clusters. The …
S3GCL | Proceedings of the 41st International Conference on Machine ...
Jul 21, 2024 · Graph Contrastive Learning (GCL) has emerged as a highly effective self-supervised approach in graph representation learning. However, prevailing GCL methods confront two primary …
A Comprehensive Introduction to Graph Neural Networks (GNNs)
Jul 21, 2022 · Building a Graph Neural Network with Pytorch We will build and train Spectral Graph Convolution for a node classification model. The code source is available in this DataLab workbook …
A novel autism spectrum disorder identification method: spectral graph ...
Oct 10, 2023 · In particular, it constructs a brain-aware representation learning network by fusing an improved graph pooling strategy and spectral graph convolution to learn subgraph structures and …
Graph coarsening: from scientific computing to machine learning
Jan 10, 2022 · The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in …
GitHub - conor-horgan/spectrai: spectrai: a deep learning framework …
Spectral acquisition and imaging play important roles across numerous fields such as machine vision, remote sensing, and biomedical imaging. While there is much potential for spectral deep learning …
Spatial-spectral graph convolutional extreme learning machine for ...
Hyperspectral image has excellent spectral information and abundant spatial information, and its feature quality is one of the key factors affecting the classification performance. Extreme learning machine …