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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 32313240 of 9051 papers

TitleStatusHype
Optimal navigability of weighted human brain connectomes in physical space0
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression0
Hierarchical Pruning of Deep Ensembles with Focal DiversityCode0
JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language ProcessingCode0
GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language UnderstandingCode0
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations0
The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic TextCode0
Program-Aided Reasoners (better) Know What They KnowCode0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
Attribute Diversity Determines the Systematicity Gap in VQACode0
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