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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

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Showing 4791–4800 of 9051 papers

TitleStatusHype
ProTA: Probabilistic Token Aggregation for Text-Video Retrieval—0
Protected group bias and stereotypes in Large Language Models—0
Protein residue networks from a local search perspective—0
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
Prototype Discovery using Quality-Diversity—0
Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning—0
Provable Hierarchy-Based Meta-Reinforcement Learning—0
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples—0
Provenance-Based Assessment of Plans in Context—0
Provenance-Based Interpretation of Multi-Agent Information Analysis—0
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