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 201–225 of 537 papers

TitleStatusHype
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
Explainable AI using expressive Boolean formulas—0
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation—0
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis—0
Interpretability with full complexity by constraining feature information—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
A hybrid machine learning framework for analyzing human decision making through learning preferences—0
Expert Study on Interpretable Machine Learning Models with Missing Data—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis—0
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More—0
Interpretable Predictive Maintenance for Hard Drives—0
Interpretability of machine learning based prediction models in healthcare—0
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity—0
Show:102550
← PrevPage 9 of 22Next →

Benchmark Results

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