SOTAVerified

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
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
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
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window TransformerCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
A Unified Approach to Interpreting Model PredictionsCode1
How Interpretable and Trustworthy are GAMs?Code1
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
Born-Again Tree EnsemblesCode1
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Benchmark Results

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