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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 1771–1780 of 9051 papers

TitleStatusHype
AI in Support of Diversity and Inclusion—0
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code—0
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study—0
Data Measurements for Decentralized Data Markets—0
A Survey on Self-Evolution of Large Language Models—0
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality—0
AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems—0
A Survey on Backbones for Deep Video Action Recognition—0
A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions—0
AI Fairness for People with Disabilities: Point of View—0
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