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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 526–537 of 537 papers

TitleStatusHype
Interpretable Explanations of Black Boxes by Meaningful PerturbationCode0
Towards A Rigorous Science of Interpretable Machine Learning—0
"What is Relevant in a Text Document?": An Interpretable Machine Learning ApproachCode0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
GENESIM: genetic extraction of a single, interpretable modelCode0
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance—0
Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability—0
Interpretable Machine Learning Models for the Digital Clock Drawing Test—0
Interpretable Two-level Boolean Rule Learning for Classification—0
Understanding Neural Networks Through Deep VisualizationCode0
Supersparse Linear Integer Models for Optimized Medical Scoring SystemsCode0
Predictive learning via rule ensembles—0
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Benchmark Results

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