Interpreting and Improving Deep-Learning Models with Reality Checks
Chandan Singh, Wooseok Ha, Bin Yu
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
Abstract Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter covers recent work aiming to interpret models by attributing importance to features and feature groups for a single prediction. Importantly, the proposed attributions assign importance to interactions between features, in addition to ...
features in isolation. These attributions are shown to yield insights across real-world domains, including bio-imaging, cosmology image and natural-language processing. We then show how these attributions can be used to directly improve the generalization of a neural network or to distill it into a simple model. Throughout the chapter, we emphasize the use of reality checks to scrutinize the proposed interpretation techniques. (Code for all methods in this chapter is available at "Image missing" github.com/csinva and "Image missing" github.com/Yu-Group , implemented in PyTorch [54]).
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Abstract Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability.
Implementation Evidence Summary
aymericdamien/TopDeepLearning is the closest maintained adjacent implementation (Matches contextual method/domain keyword: deep learning). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 6142 GitHub stars.
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Utility signals: depth 70/100, grounding 75/100, status medium.
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- aymericdamien/TopDeepLearningAdjacentConfidence: MediumStars: 6,142
Matches contextual method/domain keyword: deep learning
- jphall663/awesome-machine-learning-interpretabilityAdjacentConfidence: MediumStars: 4,029
Matches contextual method/domain keyword: interpretability
- mdozmorov/MachineLearning_notesAdjacentConfidence: MediumStars: 562
Matches contextual method/domain keyword: deep learning
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Research context
1
Citations
100
References
Tasks
Interpretability, Computer science, Generalization, Deep learning, Artificial neural network, Code (set theory), Feature (linguistics)
Methods
None detected
Domains
Artificial intelligence, Image (mathematics), Machine learning
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