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Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

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Showing 531–537 of 537 papers

TitleStatusHype
Causal rule ensemble approach for multi-arm data—0
Causality Learning: A New Perspective for Interpretable Machine Learning—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning—0
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures—0
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified