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 451–460 of 537 papers

TitleStatusHype
Efficient Learning of Interpretable Classification Rules—0
A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques—0
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition—0
Deducing neighborhoods of classes from a fitted model—0
Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data—0
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
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Benchmark Results

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