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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 3140 of 9051 papers

TitleStatusHype
3D Scene Generation: A SurveyCode4
ActionStudio: A Lightweight Framework for Data and Training of Large Action ModelsCode4
Distill Any Depth: Distillation Creates a Stronger Monocular Depth EstimatorCode4
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANsCode4
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQLCode4
Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent ExplorationCode4
Expressive Whole-Body 3D Gaussian AvatarCode4
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy PredictionCode4
Quality-aware Masked Diffusion Transformer for Enhanced Music GenerationCode4
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single ImageCode4
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