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09/15/2023
Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization
The pursuit of long-term autonomy mandates that robotic agents must cont...
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09/09/2023
Implementation of Autonomous Supply Chains for Digital Twinning: a Multi-Agent Approach
Trade disruptions, the pandemic, and the Ukraine war over the past years...
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08/15/2023
Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening
Machine unlearning, the ability for a machine learning model to forget, ...
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07/22/2023
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
Organisations often struggle to identify the causes of change in metrics...
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07/22/2023
Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem
Heavy goods vehicles are vital backbones of the supply chain delivery sy...
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05/19/2023
Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions
While the increased use of AI in the manufacturing sector has been widel...
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10/21/2022
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Supplier selection and order allocation (SSOA) are key strategic decisio...
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10/19/2022
A network science approach to identify disruptive elements of an airline
Nowadays, flight delays are quite notorious and propagate from an origin...
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10/19/2022
Exploitation of material consolidation trade-offs in a multi-tier complex supply networks
While consolidation strategies form the backbone of many supply chain op...
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09/19/2022
Trolley optimisation: An extension of bin packing to load PCB components
A trolley is a container for loading printed circuit board (PCB) compone...
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02/25/2022
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection
Despite numerous studies of deep autoencoders (AEs) for unsupervised ano...
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02/25/2022
Do autoencoders need a bottleneck for anomaly detection?
A common belief in designing deep autoencoders (AEs), a type of unsuperv...
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10/19/2021
Coalitional Bayesian Autoencoders – Towards explainable unsupervised deep learning
This paper aims to improve the explainability of Autoencoder's (AE) pred...
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09/03/2021
Will bots take over the supply chain? Revisiting Agent-based supply chain automation
Agent-based systems have the capability to fuse information from many di...
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07/28/2021
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection
After an autoencoder (AE) has learnt to reconstruct one dataset, it migh...
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07/28/2021
Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System
Recent advancements in predictive machine learning has led to its applic...
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07/28/2021
Bayesian Autoencoders for Drift Detection in Industrial Environments
Autoencoders are unsupervised models which have been used for detecting ...
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07/22/2021
Data Considerations in Graph Representation Learning for Supply Chain Networks
Supply chain network data is a valuable asset for businesses wishing to ...
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07/02/2021
Reinforcement Learning Provides a Flexible Approach for Realistic Supply Chain Safety Stock Optimisation
Although safety stock optimisation has been studied for more than 60 yea...
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06/09/2021
Understanding Softmax Confidence and Uncertainty
It is often remarked that neural networks fail to increase their uncerta...
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11/02/2020
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions
Digital Twin was introduced over a decade ago, as an innovative all-enco...
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05/15/2019
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
A simple, flexible approach to creating expressive priors in Gaussian pr...
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10/12/2018
Uncertainty in Neural Networks: Bayesian Ensembling
Understanding the uncertainty of a neural network's (NN) predictions is ...
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02/20/2018
