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Towards Falsifiable Interpretability Research in Machine Learning - Lecture
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- 1 Introduction
- 2 Outline
- 3 Obstacles
- 4 Misdirection of Saliency
- 5 What is Saliency
- 6 Saliency axioms
- 7 Input invariants
- 8 Model parameter randomization
- 9 Does silencing help humans
- 10 Takeaways
- 11 Case Study 2
- 12 Individual neurons
- 13 Activation maximization
- 14 Populations
- 15 Selective units
- 16 Ablating selective units
- 17 Posthoc studies
- 18 Regularizing selectivity
- 19 Ingenerative models
- 20 Summary
- 21 Building better hypothesis hypotheses
- 22 Building a stronger hypothesis
- 23 Key takeaways
