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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 55815590 of 9051 papers

TitleStatusHype
Testing Determinantal Point Processes0
Testing High-dimensional Multinomials with Applications to Text Analysis0
Testing macroecological theories in cryptocurrency market: neutral models can not describe diversity patterns and their variation0
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning0
Test-Optional Admissions0
Test-time Adaptation for Cross-modal Retrieval with Query Shift0
Test-Time Intensity Consistency Adaptation for Shadow Detection0
Text2Immersion: Generative Immersive Scene with 3D Gaussians0
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization0
Text Complexity And Linguistic Features: Is The Relationship Multilingual?0
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