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 281–290 of 537 papers

TitleStatusHype
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
Predictive learning via rule ensembles—0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning—0
Quantifying and Learning Disentangled Representations with Limited Supervision—0
Ranking Facts for Explaining Answers to Elementary Science Questions—0
Rapid Shear Capacity Prediction of TRM-Strengthened Unreinforced Masonry Walls through Interpretable Machine Learning using a Web App—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Reducing Optimism Bias in Incomplete Cooperative Games—0
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Benchmark Results

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