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 201–250 of 537 papers

TitleStatusHype
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Interpretable Machine Learning in Physics: A Review—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
Interpretable Reinforcement Learning with Ensemble Methods—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Knowledge Discovery from Atomic Structures using Feature Importances—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
High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture—0
Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—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
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
Learning Model Agnostic Explanations via Constraint Programming—0
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations—0
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model—0
Data-driven Approach for Static Hedging of Exchange Traded Options—0
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles—0
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity—0
Insights into the origin of halo mass profiles from machine learning—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Interpretability of machine learning based prediction models in healthcare—0
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
Brain Age from the Electroencephalogram of Sleep—0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
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Benchmark Results

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