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Found 6 papers, 2 papers with code
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data.
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains.
Pooling in Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems.
Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona
The synergy between machine learning and this enormous amount of data has the potential, still largely unexploited, to advance our understanding of the Sun and extend the capabilities of heliophysics missions.
Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites, the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since2010.
Contrastive Structured Anomaly Detection for Gaussian Graphical Models
In order to detect structural anomalies in a GGM, we consider the problem of estimating changes in the precision matrix of the corresponding Gaussian distribution.
