Summary
This paper presents a method to improve the interpretability of Machine Learning (ML) models, particularly in manufacturing, addressing a core challenge in Explainable AI (XAI). It utilizes Knowledge Graphs (KGs) to store domain-specific data, ML results, and their explanations, creating a structured link between domain knowledge and ML insights. This approach aims to make complex ML models more transparent and user-friendly by providing accessible explanations.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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