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Professor Hima Lakkaraju presents some of the latest advancements in In the first segment of the workshop, Professor Hima Lakkaraju motivates the need for interpretable machine learning in order to ... February 17, 2023 Q. Vera Liao of Microsoft Research Artificial Intelligence technologies are increasingly used to aid human ... Feature Attributions and Counterfactual Explanations Can Be Manipulated Professor Hima Lakkaraju discusses the many future research directions for building Prof. Romain Giot, University of Bordeaux, France Deep Learning is omnipresent both in academic research and industrial ...
Evaluation of Saliency based Explainability Methods Barbara Liskov MIT CSAIL October 16, 2019 Barbara Liskov was already breaking new ground in 1968, when she became one of ... The professional version of this graduate course, XCS224N Natural Language Processing with Deep Learning, runs June ...
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Stanford Seminar - ML Explainability Part 3 I Post hoc Explanation Methods
Stanford Seminar - ML Explainability Part 4 I Evaluating Model Interpretations/Explanations
Stanford Seminar - ML Explainability Part 1 I Overview and Motivation for Explainability
Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 3: Architectures
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Last Updated: May 22, 2026
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