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 326–350 of 537 papers

TitleStatusHype
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behaviorCode0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events dataCode0
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning—0
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Trees with Attention for Set Prediction TasksCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Learning Gradual Argumentation Frameworks using Genetic AlgorithmsCode0
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Developing a Fidelity Evaluation Approach for Interpretable Machine LearningCode0
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU PatientsCode0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Full interpretable machine learning in 2D with inline coordinates—0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray ImagesCode0
Optimal Counterfactual Explanations in Tree EnsemblesCode1
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
An exact counterfactual-example-based approach to tree-ensemble models interpretabilityCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Analysis and classification of main risk factors causing stroke in Shanxi Province—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Show:102550
← PrevPage 14 of 22Next →

Benchmark Results

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