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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 43614370 of 9051 papers

TitleStatusHype
Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model0
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models0
DiffCap: Exploring Continuous Diffusion on Image Captioning0
BERT for Target Apps Selection: Analyzing the Diversity and Performance of BERT in Unified Mobile Search0
Improving the Estimation of Attenuation in Q/V Band Systems with a Kalman-Based Scintillation Filter0
Improving the performance of weak supervision searches using data augmentation0
Improving vision-language alignment with graph spiking hybrid Networks0
Leveraging LLMs for Influence Path Planning in Proactive Recommendation0
Cap2Aug: Caption guided Image to Image data Augmentation0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection0
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