3 papers
cs.CV2023
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
Nataliia Molchanova, Vatsal Raina, Andrey Malinin +7
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segment…
cs.LG2023
Uncovering Unique Concept Vectors through Latent Space Decomposition
Mara Graziani, Laura O' Mahony, An-Phi Nguyen +2
Interpreting the inner workings of deep learning models is crucial for establishing trust and ensuring model safety. Concept-based explanations have emerged as a superior approach…
cs.CV2023
Disentangling Neuron Representations with Concept Vectors
Laura O'Mahony, Vincent Andrearczyk, Henning Muller +1
Mechanistic interpretability aims to understand how models store representations by breaking down neural networks into interpretable units. However, the occurrence of polysemantic…