SOTAVerified

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 501–510 of 537 papers

TitleStatusHype
RISE: Randomized Input Sampling for Explanation of Black-box ModelsCode1
Contrastive Explanations with Local Foil TreesCode0
Hierarchical interpretations for neural network predictionsCode1
Learning Kolmogorov Models for Binary Random Variables—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
Brain Age from the Electroencephalogram of Sleep—0
Probing hidden spin order with interpretable machine learningCode0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
Manipulating and Measuring Model InterpretabilityCode0
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Benchmark Results

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