Search Results for author:
Found 6 papers, 2 papers with code
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection
Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly detection, AEs still lack a way to express uncertainty in their predictions, crucial for ensuring safe and trustworthy machine learning systems in high-stake applications.
Do autoencoders need a bottleneck for anomaly detection?
Learning the identity function renders the AEs useless for anomaly detection.
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning
This paper aims to improve the explainability of Autoencoder's (AE) predictions by proposing two explanation methods based on the mean and epistemic uncertainty of log-likelihood estimate, which naturally arise from the probabilistic formulation of the AE called Bayesian Autoencoders (BAE).
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection
After an autoencoder (AE) has learnt to reconstruct one dataset, it might be expected that the likelihood on an out-of-distribution (OOD) input would be low.
Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System
In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario.
Bayesian Autoencoders for Drift Detection in Industrial Environments
Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments.
