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 171–180 of 537 papers

TitleStatusHype
Category-Specific Topological Learning of Metal-Organic Frameworks—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
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Extract Local Inference Chains of Deep Neural Nets—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
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Benchmark Results

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