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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–110 of 537 papers

TitleStatusHype
Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
A Semiparametric Approach to Interpretable Machine Learning—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning—0
A Learning Theoretic Perspective on Local Explainability—0
Show:102550
← PrevPage 11 of 54Next →

Benchmark Results

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