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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 191200 of 9051 papers

TitleStatusHype
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
in2IN: Leveraging individual Information to Generate Human INteractionsCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language ModelsCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
Integrate Any Omics: Towards genome-wide data integration for patient stratificationCode2
Knowledge Graph-Guided Retrieval Augmented GenerationCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
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