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 11–20 of 537 papers

TitleStatusHype
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
Do Feature Attribution Methods Correctly Attribute Features?Code1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Axiomatic Attribution for Deep NetworksCode1
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Benchmark Results

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