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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 33413350 of 9051 papers

TitleStatusHype
Corpus COFLA: A research corpus for the Computational study of Flamenco Music0
Corpus-based Content Construction0
Attention in Diffusion Model: A Survey0
Corporate Social Responsibility and Corporate Governance: A cognitive approach0
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans0
Coreset Selection for Object Detection0
Coreference in Spoken vs. Written Texts: a Corpus-based Analysis0
Coreference Chains Categorization by Sequence Clustering0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning0
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