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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 27312740 of 9051 papers

TitleStatusHype
DELINE8K: A Synthetic Data Pipeline for the Semantic Segmentation of Historical DocumentsCode0
Diversity Enhanced Narrative Question Generation for StorybooksCode0
DeLiGAN : Generative Adversarial Networks for Diverse and Limited DataCode0
ACD-DE: An adaptive cluster division Differential Evolution for mitigating population diversity deficiencyCode0
Fast Texture Synthesis via Pseudo OptimizerCode0
Bayesian identification of bacterial strains from sequencing dataCode0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
Looking for a Handsome Carpenter! Debiasing GPT-3 Job AdvertisementsCode0
Feasible Recourse Plan via Diverse InterpolationCode0
Federated Neural Topic ModelsCode0
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