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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 241250 of 9051 papers

TitleStatusHype
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language ModelsCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
Classifier-Free Diffusion GuidanceCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality EstimationCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
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