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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 46714680 of 9051 papers

TitleStatusHype
IFDID: Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG0
Towards standardizing Korean Grammatical Error Correction: Datasets and AnnotationCode1
A single-cell gene expression language modelCode1
S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAMCode1
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity AlgorithmsCode1
Item-based Variational Auto-encoder for Fair Music RecommendationCode1
The Robustness Limits of SoTA Vision Models to Natural Variation0
LANS: Large-scale Arabic News Summarization Corpus0
Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain DomainsCode0
Multi-Objective GFlowNetsCode1
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