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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 461–470 of 537 papers

TitleStatusHype
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Exploring Interpretability for Predictive Process Analytics—0
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
A Decision-Theoretic Approach for Model Interpretability in Bayesian FrameworkCode0
Topological data analysis of zebrafish patterns—0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
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Benchmark Results

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