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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 110 of 9051 papers

TitleStatusHype
MinerU: An Open-Source Solution for Precise Document Content ExtractionCode16
olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language ModelsCode11
Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image AnimationCode9
AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait AnimationCode9
Depth Anything V2Code9
Better Synthetic Data by Retrieving and Transforming Existing DatasetsCode7
Improving Sample Quality of Diffusion Models Using Self-Attention GuidanceCode7
Flow-GRPO: Training Flow Matching Models via Online RLCode7
FoundationStereo: Zero-Shot Stereo MatchingCode7
Adaptive In-conversation Team Building for Language Model AgentsCode7
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