2023
NIPS
NeurIPS 2023
VeriX: Towards Verified Explainability of Deep Neural Networks
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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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning
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Keyword Pioneer
— robust explanation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio