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 511–520 of 537 papers

TitleStatusHype
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning—0
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
Comparing interpretability and explainability for feature selection—0
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling—0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq—0
Show:102550
← PrevPage 52 of 54Next →

Benchmark Results

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