1 citations · 1 across the 3 of their papers we have counts for
4 papers
BenchECG and xECG: a benchmark and baseline for ECG foundation models
Riccardo Lunelli, Angus Nicolson, Samuel Martin Pröll +3
Electrocardiograms (ECGs) are inexpensive, widely used, and well-suited to deep learning. Recently, interest has grown in developing foundation models for ECGs - models that genera…
TextCAVs: Debugging vision models using text
Angus Nicolson, Yarin Gal, J. Alison Noble
Concept-based interpretability methods are a popular form of explanation for deep learning models which provide explanations in the form of high-level human interpretable concepts.…
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
Stephen Casper, Jieun Yun, Joonhyuk Baek +13
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying…
Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors
Angus Nicolson, Lisa Schut, J. Alison Noble +1
Concept-based explanations translate the internal representations of deep learning models into a language that humans are familiar with: concepts. One popular method for finding co…