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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 41–50 of 537 papers

TitleStatusHype
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable LearningCode1
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
Graph Learning for Numeric PlanningCode1
Fast Sparse Decision Tree Optimization via Reference EnsemblesCode1
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
Interpretable Machine Learning Approaches to Prediction of Chronic HomelessnessCode1
A Unified Approach to Interpreting Model PredictionsCode1
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Benchmark Results

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