Feb 1, 2020 · We propose a novel hybrid unsupervised AD method, which first integrates convolutional auto-encoder and Gaussian process regression to extract features and to ...
Oct 22, 2024 · We propose a Multi Scale Convolutional Variational Autoencoder (MSCVAE) to detect anomalies in multivariate time series data.
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Robust deep auto-encoding Gaussian process regression for unsupervised anomaly detection · Computer Science. Neurocomputing · 2020.
Nov 8, 2023 · This paper proposes a feedback channel between the Variational Auto Encoder and the Gaussian process to enhance its data feature extraction capabilities.
In this work, we propose RobustRealNVP, a robust deep density estimation framework for unsupervised ... Robust deep auto-encoding Gaussian process regres- sion ...
In this work, we propose RobustRealNVP, a robust deep density estimation framework for unsupervised anomaly detection. Our approach differs from existing flow- ...
Cao, “Robust deep auto-encoding. Gaussian process regression for unsupervised anomaly detection,” Neurocomputing, vol. 376, pp. 180–190, 2020. [86] M. L. ...
Jun 6, 2022 · Detailed structural comparison of the algorithms. The deep architecture models include Deep Autoencoding. Gaussian Mixture Model (DAGMM), ...
An end-to-end trained deep neural network that leverages Gaussian Mixture Modeling to perform density estimation and unsupervised anomaly detection.
Missing: regression | Show results with:regression
Robust deep auto-encoding Gaussian process regression for unsupervised anomaly detection. Neurocomputing 376, 180–190. doi: 10.1016/j.neucom.2019.09.078.