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 276–300 of 537 papers

TitleStatusHype
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models—0
Predicting Many Crystal Properties via an Adaptive Transformer-based Framework—0
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
Predictive learning via rule ensembles—0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning—0
Quantifying and Learning Disentangled Representations with Limited Supervision—0
Ranking Facts for Explaining Answers to Elementary Science Questions—0
Rapid Shear Capacity Prediction of TRM-Strengthened Unreinforced Masonry Walls through Interpretable Machine Learning using a Web App—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Reducing Optimism Bias in Incomplete Cooperative Games—0
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)—0
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower—0
Rethinking Interpretability in the Era of Large Language Models—0
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning—0
Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data—0
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images—0
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena—0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures—0
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Benchmark Results

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