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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 19912000 of 9051 papers

TitleStatusHype
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial NetworksCode0
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian LanguagesCode0
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in TransformersCode0
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island CoevolutionCode0
IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil , Malayalam and EnglishCode0
Colorful Image ColorizationCode0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
A Unified Substrate for Body-Brain Co-evolutionCode0
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN FrameworkCode0
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media commentsCode0
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