65 citations · 199 across the 12 of their papers we have counts for
14 papers
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…
Will bots take over the supply chain? Revisiting Agent-based supply chain automation
Liming Xu, Stephen Mak, Alexandra Brintrup
Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable…
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…
Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System
Bang Xiang Yong, Alexandra Brintrup
Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without…
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…