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Found 7 papers, 3 papers with code
Open-Source Drift Detection Tools in Action: Insights from Two Use Cases
Data drifts pose a critical challenge in the lifecycle of machine learning (ML) models, affecting their performance and reliability.
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction
Leveraging SHAP clustering, our method provides interpretable explanations for model predictions and uses these insights to adaptively refine the model, balancing model complexity with predictive performance.
XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ computer vision and machine learning for precise, rapid defect detection.
AutoCure: Automated Tabular Data Curation Technique for ML Pipelines
Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance.
REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
In this work, we introduce a comprehensive benchmark, called REIN1, to thoroughly investigate the impact of data cleaning methods on various ML models.
DiffML: End-to-end Differentiable ML Pipelines
In this paper, we present our vision of differentiable ML pipelines called DiffML to automate the construction of ML pipelines in an end-to-end fashion.
