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 171–180 of 537 papers

TitleStatusHype
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsCode0
Challenging common interpretability assumptions in feature attribution explanationsCode0
Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesCode0
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning ModelsCode0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
GENESIM: genetic extraction of a single, interpretable modelCode0
Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine LearningCode0
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?Code0
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Benchmark Results

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