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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 91100 of 9051 papers

TitleStatusHype
MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary ProgrammingCode2
ZIPA: A family of efficient models for multilingual phone recognitionCode2
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational PathologyCode2
NoisyRollout: Reinforcing Visual Reasoning with Data AugmentationCode2
SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World UsersCode2
MegaMath: Pushing the Limits of Open Math CorporaCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
Modifying Large Language Model Post-Training for Diverse Creative WritingCode2
PET-MAD, a universal interatomic potential for advanced materials modelingCode2
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