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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
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods In Psychiatry Detection Applications, Specifically Depression Disorder: A Brief Review.—0
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review—0
Topological data analysis of zebrafish patterns—0
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications—0
Toward More Generalized Malicious URL Detection Models—0
Towards Analogy-Based Explanations in Machine Learning—0
Towards A Rigorous Science of Interpretable Machine Learning—0
Interpretable Machine Learning: Moving From Mythos to Diagnostics—0
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Towards making NLG a voice for interpretable Machine Learning—0
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Benchmark Results

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