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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 431–440 of 537 papers

TitleStatusHype
Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case StudyCode0
ProtoAttend: Attention-Based Prototypical LearningCode0
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networksCode0
System Design for a Data-driven and Explainable Customer Sentiment MonitorCode0
Explaining How Deep Neural Networks Forget by Deep VisualizationCode0
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine LearningCode0
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic modelsCode0
Interpretable Explanations of Black Boxes by Meaningful PerturbationCode0
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine LearningCode0
Verifying Properties of Tsetlin MachinesCode0
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Benchmark Results

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