Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...
Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

Anomaly detection is the process of finding items in a dataset that are different in some way from the majority of the items. For example, you could examine a dataset of credit card transactions to ...
GitHub

LSTM Autoencoder-based Anomaly Detection

This project is a personal practice attempt at performing anomaly detection on a multivariate time-series dataset. The dataset chosen is the Tennessee Eastman dataset, and anomalies in the data are ...
IEEE

Anomaly Detection in Complex Systems Using Extended Isolation Forest and Denoising Autoencoder

Abstract: In this paper, we propose an anomaly detection model based on Extended Isolation Forest and Denoising Autoencoder, which achieves unsupervised anomaly detection with good generalization ...
GitHub

SVG-T2I: Scaling up Text-to-Image Latent Diffusion Model

Important Note: This repository implements SVG-T2I, a text-to-image diffusion framework that performs visual generation directly in Visual Foundation Model (VFM) representation space, rather than ...
Frontiers

Subtyping insomnia disorder with a population graph attention autoencoder: revealing two distinct biotypes

Insomnia disorder (ID) is neurobiologically heterogeneous and often eludes characterization by traditional group-level neuroimaging. Subtyping based on neuroimaging and clinical data offers a ...