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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 15811590 of 9051 papers

TitleStatusHype
k-mer-based approaches to bridging pangenomics and population genetics0
Vista3D: Unravel the 3D Darkside of a Single ImageCode2
EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning0
Massively Multi-Person 3D Human Motion Forecasting with Scene ContextCode1
In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator LearningCode0
Multivariate Analysis of Gut Microbiota Composition and Prevalence of Gastric Cancer0
A Chinese Continuous Sign Language Dataset Based on Complex Environments0
Enhancing Complex Formula Recognition with Hierarchical Detail-Focused Network0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation0
RopeBEV: A Multi-Camera Roadside Perception Network in Bird's-Eye-View0
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