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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 276–300 of 537 papers

TitleStatusHype
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
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs—0
Leveraging advances in machine learning for the robust classification and interpretation of networks—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
Interpreting a Machine Learning Model for Detecting Gravitational Waves—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
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