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The Ethereal
VeriX: Towards Verified Explainability of Deep Neural Networks
December 02, 2022 ยท Declared Dead ยท ๐ Neural Information Processing Systems
"No code URL or promise found in abstract"
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Authors
Min Wu, Haoze Wu, Clark Barrett
arXiv ID
2212.01051
Category
cs.LG: Machine Learning
Citations
25
Venue
Neural Information Processing Systems
Last Checked
3 months ago
Abstract
We present VeriX (Verified eXplainability), a system for producing optimal robust explanations and generating counterfactuals along decision boundaries of machine learning models. We build such explanations and counterfactuals iteratively using constraint solving techniques and a heuristic based on feature-level sensitivity ranking. We evaluate our method on image recognition benchmarks and a real-world scenario of autonomous aircraft taxiing.
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