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Interpretability for Deep Learning: Theory, Applications and Scientific Insights

Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube

Tags

Deep Learning Courses Machine Learning Courses Neural Networks Courses Language Models Courses Explainable AI Courses Interpretability Courses

Course Description

Overview

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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.

Syllabus

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


Taught by

Institute for Pure & Applied Mathematics (IPAM)

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