83 citations · 266 across the 39 of their papers we have counts for
40 papers
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…
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation
Jack Furby, Daniel Cunnington, Dave Braines +1
Concept Bottleneck Models (CBMs) first map raw input(s) to a vector of human-defined concepts, before using this vector to predict a final classification. We might therefore expect…
AAAI FSS-21: Artificial Intelligence in Government and Public Sector Proceedings
Mihai Boicu, Erik Blasch, Alun Preece
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Washington, DC, USA, November 4-6, 2021
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification
Aleksandra Edwards, Asahi Ushio, Jose Camacho-Collados +2
Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity. State-of-the-art generat…
Using DeepProbLog to perform Complex Event Processing on an Audio Stream
Marc Roig Vilamala, Tianwei Xing, Harrison Taylor +6
In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymboli…