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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 37013710 of 9051 papers

TitleStatusHype
A Taxonomy of Adaptive Traffic Signal Control0
TSMD: A Database for Static Color Mesh Quality Assessment Study0
Path Shadowing Monte-CarloCode1
Are Easy Data Easy (for K-Means)0
Feature-aware conditional GAN for category text generation0
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning0
A Novel Cross-Perturbation for Single Domain Generalization0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson ApproachesCode0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment0
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