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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

Showing 26112620 of 9051 papers

TitleStatusHype
CapOnImage: Context-driven Dense-Captioning on Image0
A Novel ILP Framework for Summarizing Content with High Lexical Variety0
A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias0
CAPA: Continuous-Aperture Arrays for Revolutionizing 6G Wireless Communications0
A novel hybrid FSO / RF communication system with receive diversity0
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?0
A Novel Ensemble Learning Approach to Unsupervised Record Linkage0
Adversarial Diversity and Hard Positive Generation0
5G framework concepts for the next generation networks0
Is News Recommendation a Sequential Recommendation Task?0
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