GitHub

GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection

Before you begin, ensure that you have Anaconda or Miniconda installed on your system. This guide assumes that you have a CUDA-enabled GPU. Benchmark the GCN model on the example Reddit dataset under ...
note

[3 Steps] A Practical Roadmap for 30-something Field Engineers to Master Python at Maximum Speed and Implement Predictive Maintenance AI in One Month

Time-series data in manufacturing (temperature, pressure, vibration, current...) is tricky. Data preprocessing, windowing, normalization, the format to pass to the model... "I'll visualize that data ...
Scientific Research Publishing

Machine Learning-Based Outlier Detection in Long-Term Climate Data: Evidence from Burkina Faso’s Synoptic Network ()

Laboratoire de Matériaux et Environnement (LAME), Université Joseph Ki-Zerbo, Ouagadougou, Burkina Faso. In recent decades, the impact of climate change on natural resources has increased. However, ...
PNAS

Spin-informed universal graph neural networks for simulating magnetic ordering

The development of universal machine-learning interatomic potentials capable of simulating magnetic ordering is vital for the in silico discovery of indispensable magnetic materials across vast ...
Frontiers

ARMA model development and analysis for global temperature uncertainty

Temperature uncertainty can have a significant impact on astronomical research in several ways. Observations made using telescopes and other astronomical instruments are often temperature sensitive.
GitHub

Graph Neural Network-Based Anomaly Detection (GNNAD)

gnnad is a package for anomaly detection on multivariate time series data. This model builds on the recently-proposed Graph Deviation Network (GDN) 1, a graph neural network model that uses embeddings ...