4 citations · 18 across the 14 of their papers we have counts for
29 papers
Explainability in Machine Learning: a Pedagogical Perspective
Andreas Bueff, Ioannis Papantonis, Auste Simkute +1
Given the importance of integrating of explainability into machine learning, at present, there are a lack of pedagogical resources exploring this. Specifically, we have found a nee…
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Xin Du, Benedicte Legastelois, Bhargavi Ganesh +5
Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and othe…
Principled Diverse Counterfactuals in Multilinear Models
Ioannis Papantonis, Vaishak Belle
Machine learning (ML) applications have automated numerous real-life tasks, improving both private and public life. However, the black-box nature of many state-of-the-art models po…
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks
Nicholas Hoernle, Rafael Michael Karampatsis, Vaishak Belle +1
We propose a novel way to incorporate expert knowledge into the training of deep neural networks. Many approaches encode domain constraints directly into the network architecture,…
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Christian Muise, Vaishak Belle, Paolo Felli +4
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, pla…
One Down, 699 to Go: or, synthesising compositional desugarings
Sándor Bartha, James Cheney, Vaishak Belle
Programming or scripting languages used in real-world systems are seldom designed with a formal semantics in mind from the outset. Therefore, developing well-founded analysis tools…