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IEEE7mon
Targeted Collapse Regularized Autoencoder for Anomaly Detection: Black Hole at the Center
Abstract: Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the autoencoder ...
Visual Studio Magazine4y
Autoencoder Anomaly Detection Using PyTorch
Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...
IEEE4y
Brief Industry Paper: HDAD: Hyperdimensional Computing-based Anomaly Detection for Automotive Sensor Attacks
In this paper, we develop HDAD, a hyperdimensional computing-based anomaly detection method. Hyperdimensional computing (HDC) is an emerging brain-inspired computing paradigm that mimics the brain ...
Oak Ridge National Lab1y
Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model
To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory ...
Visual Studio Magazine4y
Autoencoder Anomaly Detection Using PyTorch
Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...