83 citations · 266 across the 39 of their papers we have counts for
9 papers · 1 filter
Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?
Jack Furby, Daniel Cunnington, Dave Braines +1
Concept Bottleneck Models (CBMs) are regarded as inherently interpretable because they first predict a set of human-defined concepts which are used to predict a task label. For inh…
Knowledge from Uncertainty in Evidential Deep Learning
Cai Davies, Marc Roig Vilamala, Alun D. Preece +3
This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning a…
Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials
Iain Barclay, Alun Preece, Ian Taylor +2
Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI)…
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…