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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 161–170 of 537 papers

TitleStatusHype
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
A Survey of Malware Detection Using Deep Learning—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model—0
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning PredictionsCode0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Machine Learning for Economic Forecasting: An Application to China's GDP Growth—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
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Benchmark Results

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