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 451–475 of 537 papers

TitleStatusHype
Efficient Learning of Interpretable Classification Rules—0
A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques—0
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition—0
Deducing neighborhoods of classes from a fitted model—0
Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data—0
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity—0
Expert Study on Interpretable Machine Learning Models with Missing Data—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Explainable AI using expressive Boolean formulas—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
Show:102550
← PrevPage 19 of 22Next →

Benchmark Results

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