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 191–200 of 537 papers

TitleStatusHype
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures—0
Leveraging advances in machine learning for the robust classification and interpretation of networks—0
Interpretable Machine Learning for Survival AnalysisCode0
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data—0
Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine LearningCode0
LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Models—0
Explaining Kernel Clustering via Decision Trees—0
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems—0
Challenges in Variable Importance Ranking Under Correlation—0
Reducing Optimism Bias in Incomplete Cooperative Games—0
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Benchmark Results

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