SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–375 of 537 papers

TitleStatusHype
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles—0
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Insights into the origin of halo mass profiles from machine learning—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Interpretability of machine learning based prediction models in healthcare—0
Interpretability with full complexity by constraining feature information—0
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation—0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector—0
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons—0
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Review of Interpretable Machine Learning Models for Disease Prognosis—0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Interpretable Machine Learning for Kronecker Coefficients—0
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Benchmark Results

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