IEEE

Evolutionary Adversarial Autoencoder for Unsupervised Anomaly Detection of Industrial Internet of Things

The rapid growth of interconnected smart devices and advanced computing technologies in the industrial Internet of Things (IIoT) has significantly enhanced operational resilience and performance but ...
IEEE

Autoencoder in Autoencoder Networks

Abstract: Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised ...
Scientific Research Publishing

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications ()

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...
Nature

Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage

Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...
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 ...
Nature

ECG autoencoder based on low-rank attention

The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic ...
LinkedIn

Building a CNN Autoencoder with Keras and TensorFlow

In the world of deep learning, autoencoders are a class of neural networks designed to learn efficient representations of data, typically for the purpose of dimensionality reduction or feature ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

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 ...
GitHub

Autoencoders for Time-Series with Hyperparameter Tuning

This toolbox enables hyperparameter optimization for autoencoders using a genetic algorithm. This framework extends the framework "Generic Deep Autoencoder for Time-Series" by providing an algorithm ...
GitHub

Generic Deep Autoencoder for Time-Series

This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder consists of two, possibly deep, neural ...