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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
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Is Grad-CAM Explainable in Medical Images?—0
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans—0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
Worth of knowledge in deep learningCode0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
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Benchmark Results

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