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 351–400 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
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study—0
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations—0
Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system—0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Interpretable Machine Learning for Weather and Climate Prediction: A Survey—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
Interpretable machine learning-guided design of Fe-based soft magnetic alloys—0
Interpretable machine learning in Physics—0
Interpretable Machine Learning in Physics: A Review—0
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation—0
Interpretable machine learning models: a physics-based view—0
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems—0
Interpretable Machine Learning Models for the Digital Clock Drawing Test—0
Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach—0
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development—0
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style—0
Interpretable Predictive Maintenance for Hard Drives—0
Interpretable Reinforcement Learning with Ensemble Methods—0
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders—0
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems—0
Interpretable Two-level Boolean Rule Learning for Classification—0
GFN-SR: Symbolic Regression with Generative Flow NetworksCode0
GENESIM: genetic extraction of a single, interpretable modelCode0
Show:102550
← PrevPage 8 of 11Next →

Benchmark Results

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