Enhancing Query Optimization in Production: A Microsoft Journey
Explore Microsoft's innovative approach to query optimization in production environments, addressing challenges with general-purpose optimization and introducing specialized cloud-based optimizers. Learn about the implementation details, experiments conducted, and the solution proposed. Discover how
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Model evaluation strategy impacts the interpretation and performance of machine learning models
The evaluation strategy used for machine learning models significantly impacts their interpretation and performance. This study explores different evaluation methods and their implications for understanding climate-crop dynamics using explainable machine learning approaches. The strategy involves tr
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Understanding the Right to an Explanation in GDPR and AI Decision Making
The paper delves into the necessity for Explainable AI driven by regulations such as the GDPR, which mandates explanations for algorithmic decisions. It discusses the debate surrounding the existence of a legally binding right to explanation and the complexities of accommodating algorithmic machines
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Explainable AI Seeing Through the Code
Discover how XAI is enhancing transparency, building trust, and driving adoption in industries like healthcare, finance, and legal sectors.\n
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Enhancing Intent Classification with Chain of Thought Prompting
This study explores the use of Chain of Thought Prompting (CoT) for few-shot intent classification using large language models. The approach involves a series of reasoning steps to better understand user intent, leading to improved performance and explainable results compared to traditional promptin
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Enhancing Counterfactual Explanations for Improved Understanding
This article explores the concept of generating interpretable, diverse, and plausible counterfactual explanations within explainable AI (XAI). It highlights the challenges with current methods, introduces an instance-guided approach, and emphasizes the importance of good counterfactuals. The discuss
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Explainable Recommendation Using Attentive Multi-View Learning
The research presented at the 33rd AAAI Conference on Artificial Intelligence focuses on developing an explainable deep model for recommendation systems. It addresses challenges in extracting explicit features from noisy data and proposes a Deep Explicit Attentive Multi-View Learning Model. This mod
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