6 citations · 8 across the 4 of their papers we have counts for
6 papers · 1 filter
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
Liam Hiley, Alun Preece, Yulia Hicks +3
A small subset of explainability techniques developed initially for image recognition models has recently been applied for interpretability of 3D Convolutional Neural Network model…
Sanity Checks for Saliency Metrics
Richard Tomsett, Dan Harborne, Supriyo Chakraborty +2
Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. These methods produce estimates of the relevance of each pixel to the classifica…
Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations
Liam Hiley, Alun Preece, Yulia Hicks
The popularity of Deep Learning for real-world applications is ever-growing. With the introduction of high performance hardware, applications are no longer limited to image recogni…
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition
Liam Hiley, Alun Preece, Yulia Hicks +2
Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconst…
Quantifying Transparency of Machine Learning Systems through Analysis of Contributions
Iain Barclay, Alun Preece, Ian Taylor +1
Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engi…
Deep Q-Learning for Directed Acyclic Graph Generation
Laura D'Arcy, Padraig Corcoran, Alun Preece
We present a method to generate directed acyclic graphs (DAGs) using deep reinforcement learning, specifically deep Q-learning. Generating graphs with specified structures is an im…