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 521–530 of 537 papers

TitleStatusHype
The Promise and Peril of Human Evaluation for Model Interpretability—0
The (Un)reliability of saliency methodsCode0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
Interpretable Explanations of Black Boxes by Meaningful PerturbationCode0
Towards A Rigorous Science of Interpretable Machine Learning—0
"What is Relevant in a Text Document?": An Interpretable Machine Learning ApproachCode0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
GENESIM: genetic extraction of a single, interpretable modelCode0
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Benchmark Results

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