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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 47514760 of 9051 papers

TitleStatusHype
Probabilistic Regressor Chains with Monte Carlo Methods0
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs0
Probing Contextualized Sentence Representations with Visual Awareness0
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models0
Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study0
PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow0
Program Translation via Code Distillation0
Progress and open problems in evolutionary dynamics0
Progressive Feature Mining and External Knowledge-Assisted Text-Pedestrian Image Retrieval0
Progressive growing of self-organized hierarchical representations for exploration0
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