We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the functional connectivity features. Furthermore, an attention layer based on recurrence rate ...

This repository provides the official Python implementation our KDD 2025 paper: 📄 "Predicting the Dynamics of Complex Systems via Multiscale Diffusion Autoencoder" MDPNet is a novel approach for ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

This project provides an unsupervised learning solution for detecting anomalies in time-series data, specifically demonstrated on synthetic network traffic. It leverages a Long Short-Term Memory (LSTM ...

However, relatively little research has addressed open-set learning issues involving unknown working modes. A multifunction radar working mode open-set recognition method based on dual autoencoder ...

The reciprocity of wireless channels is a prerequisite for physical layer secret key generation (SKG). However, inherent factors, such as noise, asynchronous observations, and hardware impairments, ...