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 281–290 of 537 papers

TitleStatusHype
A Generic Approach for Reproducible Model DistillationCode0
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
Supervised Feature Compression based on Counterfactual AnalysisCode0
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning—0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine LearningCode0
Motif-guided Time Series Counterfactual Explanations—0
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Benchmark Results

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