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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 521–530 of 537 papers

TitleStatusHype
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena—0
Hidden Citations Obscure True Impact in Science—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture—0
Challenges in Variable Importance Ranking Under Correlation—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
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Benchmark Results

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