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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 44714480 of 9051 papers

TitleStatusHype
Reconfigurable Holographic Surface: A New Paradigm to Implement Holographic Radio0
Personalized Student Attribute Inference0
Link-level simulator for 5G localizationCode0
Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters0
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
Recommending on graphs: a comprehensive review from a data perspective0
Multi-Frequency Channel Modeling for Millimeter Wave and THz Wireless Communication via Generative Adversarial NetworksCode0
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?Code0
Secure and Privacy Preserving Proxy Biometrics Identities0
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