150 citations · 427 across the 25 of their papers we have counts for
28 papers
Continual Test-Time Learning with Evolution Strategies
Guangya Hao, Yiting Duan, Longyan Tan +5
Large language models increasingly operate in open-ended environments, where they are expected to continually adapt to evolving tasks and distributions, often without access to ext…
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection
Yunbo Long, Zhengyang Ling, Sam Brook +2
Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accuracy. These systems typically require ex…
Implementation of Autonomous Supply Chains for Digital Twinning: a Multi-Agent Approach
Liming Xu, Yaniv Proselkov, Stefan Schoepf +3
Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are a…
Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization
Jack Foster, Alexandra Brintrup
The pursuit of long-term autonomy mandates that machine learning models must continuously adapt to their changing environments and learn to solve new tasks. Continual learning seek…
Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening
Jack Foster, Stefan Schoepf, Alexandra Brintrup
Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, ma…
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration. This task becomes increasingly challenging when the cause li…
Using Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem
Stefan Schoepf, Stephen Mak, Julian Senoner +3
Heavy goods vehicles are vital backbones of the supply chain delivery system but also contribute significantly to carbon emissions with only 60% loading efficiency in the United Ki…
Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions
Alexandra Brintrup, George Baryannis, Ashutosh Tiwari +3
While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation. Altho…
Network science approach for identifying disruptive elements of an airline
Vinod Kumar Chauhan, Anna Ledwoch, Alexandra Brintrup +4
Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cau…
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Vinod Kumar Chauhan, Stephen Mak, Ajith Kumar Parlikad +3
Supplier selection and order allocation (SSOA) are key strategic decisions in supply chain management which greatly impact the performance of the supply chain. Although, the SSOA p…
Exploitation of material consolidation trade-offs in multi-tier complex supply networks
Vinod Kumar Chauhan, Muhannad Alomari, James Arney +2
While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an unde…
Trolley Optimisation for Loading Printed Circuit Board Components
Vinod Kumar Chauhan, Mark Bass, Ajith Kumar Parlikad +1
A trolley is a container for loading printed circuit board (PCB) components, and a trolley optimisation problem (TOP) is an assignment of PCB components to trolleys for use in the…
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…
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…
Supply Chain Digital Twin Framework Design: An Approach of Supply Chain Operations Reference Model and System of Systems
Jie Zhang, Alexandra Brintrup, Anisoara Calinescu +2
Digital twin technology has been regarded as a beneficial approach in supply chain development. Different from traditional digital twin (temporal dynamic), supply chain digital twi…
Reinforcement Learning Provides a Flexible Approach for Realistic Supply Chain Safety Stock Optimisation
Edward Elson Kosasih, Alexandra Brintrup
Although safety stock optimisation has been studied for more than 60 years, most companies still use simplistic means to calculate necessary safety stock levels, partly due to the…
Understanding Softmax Confidence and Uncertainty
Tim Pearce, Alexandra Brintrup, Jun Zhu
It is often remarked that neural networks fail to increase their uncertainty when predicting on data far from the training distribution. Yet naively using softmax confidence as a p…
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions
Angira Sharma, Edward Kosasih, Jie Zhang +2
Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation and forecasting. However,…