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 331–340 of 537 papers

TitleStatusHype
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Trees with Attention for Set Prediction TasksCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Learning Gradual Argumentation Frameworks using Genetic AlgorithmsCode0
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Developing a Fidelity Evaluation Approach for Interpretable Machine LearningCode0
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU PatientsCode0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Full interpretable machine learning in 2D with inline coordinates—0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
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Benchmark Results

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