Interpretability for Deep Learning: Theory, Applications and Scientific Insights

Explore a comprehensive lecture on deep learning interpretability presented by Oliver Eberle from Technische Universität Berlin at IPAM's Theory and Practice of Deep Learning Workshop. Delve into the importance of understanding complex decision strategies in deep learning models and the field of Explainable AI. Discover methods for improving transparency, safety, and trustworthiness in model deployment. Gain insights into techniques for revealing higher-order interactions and undesired model behavior. Learn about practical applications of these interpretability tools in scientific discovery, including early modern history of science, human alignment with language models, and histopathology. This 56-minute talk offers a thorough examination of the theory, applications, and scientific insights derived from deep learning interpretability.

Oliver Eberle - Interpretability for Deep Learning: Theory, Applications and Scientific Insights