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 201–210 of 537 papers

TitleStatusHype
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
Explainable AI using expressive Boolean formulas—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—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
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
Show:102550
← PrevPage 21 of 54Next →

Benchmark Results

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