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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 5160 of 9051 papers

TitleStatusHype
Improving Model Evaluation using SMART Filtering of Benchmark DatasetsCode3
Results of the Big ANN: NeurIPS'23 competitionCode3
Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language ModelsCode3
SkillMimic: Learning Basketball Interaction Skills from DemonstrationsCode3
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha FactorsCode3
Visible-Thermal Tiny Object Detection: A Benchmark Dataset and BaselinesCode3
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone GenerationCode3
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse LandscapesCode3
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