1 citations · 1 across the 6 of their papers we have counts for
6 papers
Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1
Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions. This misses coupling faults,…
Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1
Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settings. While reinforcement learni…
Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
Dhiraj Neupane, Richard Dazeley, Mohamed Reda Bouadjenek +1
Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL's sequential deci…
Handling Out-of-Distribution Data: A Survey
Lakpa Tamang, Mohamed Reda Bouadjenek, Richard Dazeley +1
In the field of Machine Learning (ML) and data-driven applications, one of the significant challenge is the change in data distribution between the training and deployment stages,…
Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges
Hao Xu, Arbind Agrahari Baniya, Sam Well +3
Video event detection has become a cornerstone of modern sports analytics, powering automated performance evaluation, content generation, and tactical decision-making. Recent advan…
Margin-bounded Confidence Scores for Out-of-Distribution Detection
Lakpa D. Tamang, Mohamed Reda Bouadjenek, Richard Dazeley +1
In many critical Machine Learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurate…