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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 26012610 of 9051 papers

TitleStatusHype
Distributionally-Informed Recommender System Evaluation0
Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators0
DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning0
``Caption'' as a Coherence Relation: Evidence and Implications0
CaptainGAN: Navigate Through Embedding Space For Better Text Generation0
A Novel Mathematical Framework for Objective Characterization of Ideas0
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
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