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 361–370 of 537 papers

TitleStatusHype
Interpretability with full complexity by constraining feature information—0
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation—0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector—0
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons—0
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Benchmark Results

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