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 481–490 of 537 papers

TitleStatusHype
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower—0
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans—0
YASENN: Explaining Neural Networks via Partitioning Activation Sequences—0
Explaining Kernel Clustering via Decision Trees—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Explanations for Automatic Speech Recognition—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
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Benchmark Results

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