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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 311–320 of 537 papers

TitleStatusHype
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics—0
Techniques for Interpretable Machine Learning—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Tensor Polynomial Additive Model—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
The Doctor Just Won't Accept That!—0
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR—0
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
The Partial Response Network: a neural network nomogram—0
The Promise and Peril of Human Evaluation for Model Interpretability—0
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Benchmark Results

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