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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 41814190 of 9051 papers

TitleStatusHype
Ensemble prosody prediction for expressive speech synthesis0
ReMoDiffuse: Retrieval-Augmented Motion Diffusion ModelCode2
DivClust: Controlling Diversity in Deep ClusteringCode1
MetaHead: An Engine to Create Realistic Digital Head0
The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web ArchivesCode1
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset0
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking TasksCode1
Progressive Random Convolutions for Single Domain Generalization0
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?0
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions0
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