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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 361–370 of 537 papers

TitleStatusHype
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
Grouped Feature Importance and Combined Features Effect PlotCode1
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer InformationCode0
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structureCode0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Interpretable Machine Learning: Moving From Mythos to Diagnostics—0
CoDeGAN: Contrastive Disentanglement for Generative Adversarial NetworkCode0
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Benchmark Results

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