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 141–150 of 537 papers

TitleStatusHype
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Additive Higher-Order Factorization Machines—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Challenges in Variable Importance Ranking Under Correlation—0
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Causal rule ensemble approach for multi-arm data—0
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Benchmark Results

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