65 citations · 199 across the 12 of their papers we have counts for
8 papers · 1 filter
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection
Bang Xiang Yong, Alexandra Brintrup
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…
Do autoencoders need a bottleneck for anomaly detection?
Bang Xiang Yong, Alexandra Brintrup
A common belief in designing deep autoencoders (AEs), a type of unsupervised neural network, is that a bottleneck is required to prevent learning the identity function. Learning th…
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning
Bang Xiang Yong, Alexandra Brintrup
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 e…
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection
Bang Xiang Yong, Tim Pearce, Alexandra Brintrup
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. This has been studied…
Bayesian Autoencoders for Drift Detection in Industrial Environments
Bang Xiang Yong, Yasmin Fathy, Alexandra Brintrup
Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. A typical use includes training a predictive model with data from se…
Data Considerations in Graph Representation Learning for Supply Chain Networks
Ajmal Aziz, Edward Elson Kosasih, Ryan-Rhys Griffiths +1
Supply chain network data is a valuable asset for businesses wishing to understand their ethical profile, security of supply, and efficiency. Possession of a dataset alone however…