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 61–70 of 537 papers

TitleStatusHype
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Grouped Feature Importance and Combined Features Effect PlotCode1
Axiomatic Attribution for Deep NetworksCode1
Born-Again Tree EnsemblesCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window TransformerCode1
Interpretable Machine Learning Approaches to Prediction of Chronic HomelessnessCode1
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Benchmark Results

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