33 citations · 40 across the 6 of their papers we have counts for
6 papers
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
Jamie Duell, Monika Seisenberger, Hsuan Fu +1
Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in…
Concurrent Gaussian elimination
Ulrich Berger, Monika Seisenberger, Dieter Spreen +1
Working in a semi-constructive logical system that supports the extraction of concurrent programs, we extract a program inverting non-singular real valued matrices from a construct…
On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study
Veera Raghava Reddy Kovvuri, Siyuan Liu, Monika Seisenberger +2
Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature…
Towards a Shapley Value Graph Framework for Medical peer-influence
Jamie Duell, Monika Seisenberger, Gert Aarts +2
eXplainable Artificial Intelligence (XAI) is a sub-field of Artificial Intelligence (AI) that is at the forefront of AI research. In XAI, feature attribution methods produce explan…
Adaptive Neighbourhoods for the Discovery of Adversarial Examples
Jay Morgan, Adeline Paiement, Arno Pauly +1
Deep Neural Networks (DNNs) have often supplied state-of-the-art results in pattern recognition tasks. Despite their advances, however, the existence of adversarial examples have c…
Extracting verified decision procedures: DPLL and Resolution
Ulrich Berger, Andrew Lawrence, Fredrik Nordvall Forsberg +1
This article is concerned with the application of the program extraction technique to a new class of problems: the synthesis of decision procedures for the classical satisfiability…