VI seminar 2021 #1 - Explainability and uncertainty in deep learning

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Recent advances in explainable deep learning and how to model uncertainty in explainability.

Presenter: Kristoffer Knutsen Wickstrøm, PhD candidate at UiT –The Arctic University of Norway


Kristoffer Wickstrøm will give a talk on explainability in deep learning.

Deep learning is the cornerstone of artificial intelligence applications across a wide range of tasks and domains. An important component that is missing from deep learning is explainability, i.e. the ability to explain what influenced a prediction made by a deep learning-based system. Explainable deep learning is an active area of research, with new algorithms being proposed at a rapid pace. This presentation will give a brief review of existing methods for explainable deep learning, and a more in-depth presentation of some recent methods. Furthermore, modeling uncertainty in explainability has gained less attention but is also a crucial component to design trustworthy deep learning-based systems. Recent works on modeling uncertainty in explainability will be presented, which will be exemplified with applications to medical data.

This seminar is open for members of the consortium. If you want to participate as a guest please let us know.

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