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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 151160 of 9051 papers

TitleStatusHype
Diffusion Bridge Implicit ModelsCode2
A Closer Look into Mixture-of-Experts in Large Language ModelsCode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
Test-time Alignment of Diffusion Models without Reward Over-optimizationCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
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