Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure
Probabilistic latent variable regression models have recently caught much attention in the
process industry, particularly for soft sensor applications. One of the main challenges for …
process industry, particularly for soft sensor applications. One of the main challenges for …
Connections with robust PCA and the role of emergent sparsity in variational autoencoder models
Variational autoencoders (VAE) represent a popular, exible form of deep generative model
that can be stochastically _t to samples from a given random process using an information …
that can be stochastically _t to samples from a given random process using an information …
Robust variational autoencoders for outlier detection and repair of mixed-type data
S Eduardo, A Nazábal, CKI Williams… - International …, 2020 - proceedings.mlr.press
We focus on the problem of unsupervised cell outlier detection and repair inmixed-type
tabular data. Traditional methods are concerned only with detecting which rows in the …
tabular data. Traditional methods are concerned only with detecting which rows in the …
Hidden talents of the variational autoencoder
Variational autoencoders (VAE) represent a popular, flexible form of deep generative model
that can be stochastically fit to samples from a given random process using an information …
that can be stochastically fit to samples from a given random process using an information …
Recurrent variational autoencoders for learning nonlinear generative models in the presence of outliers
This paper explores two useful modifications of the recent variational autoencoder (VAE), a
popular deep generative modeling framework that dresses traditional autoencoders with …
popular deep generative modeling framework that dresses traditional autoencoders with …
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source
Significant advances in utilizing deep learning for anomaly detection have been made in
recent years. However, these methods largely assume the existence of a normal training set …
recent years. However, these methods largely assume the existence of a normal training set …
Anomaly Detection in X-Ray Physics
RA Humble - 2023 - search.proquest.com
Anomaly detection is an important task for complex systems (eg, industrial facilities,
manufacturing, large-scale science experiments), where failures in a sub-system can lead to …
manufacturing, large-scale science experiments), where failures in a sub-system can lead to …