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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 441–450 of 537 papers

TitleStatusHype
Diagnostic-free onboard battery health assessment—0
Differentiable Genetic Programming for High-dimensional Symbolic Regression—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model—0
Towards Simple Machine Learning Baselines for GNSS RFI Detection—0
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Benchmark Results

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