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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 321–330 of 537 papers

TitleStatusHype
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Using an interpretable Machine Learning approach to study the drivers of International Migration—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics—0
Longitudinal Distance: Towards Accountable Instance Attribution—0
Techniques for Interpretable Machine Learning—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans—0
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Benchmark Results

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