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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 151–160 of 537 papers

TitleStatusHype
Causality Learning: A New Perspective for Interpretable Machine Learning—0
Causal Entropy and Information Gain for Measuring Causal Control—0
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models—0
Causal Dependence Plots—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
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Benchmark Results

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