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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 44514460 of 9051 papers

TitleStatusHype
Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic AI0
Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions0
Detail-Preserving Latent Diffusion for Stable Shadow Removal0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment0
A Data Quality Assessment Framework for AI-enabled Wireless Communication0
Innovation-exnovation dynamics on trees and trusses0
InOut: Diverse Image Outpainting via GAN Inversion0
Insect Diversity Estimation in Polarimetric Lidar0
Instruction-Driven Game Engine: A Poker Case Study0
Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study0
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