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

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Showing 291–300 of 537 papers

TitleStatusHype
Adversarial Attacks and Defenses: An Interpretation Perspective—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Interpreting Neural Ranking Models using Grad-CAM—0
Exploring Interpretability for Predictive Process Analytics—0
Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning—0
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Is Grad-CAM Explainable in Medical Images?—0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity—0
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Benchmark Results

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