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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 19311940 of 9051 papers

TitleStatusHype
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Creative Diversity: Patterns in the Creative Habits of ~10,000 PeopleCode0
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
MDIA: A Benchmark for Multilingual Dialogue Generation in 46 LanguagesCode0
Language GANs Falling ShortCode0
Commet: comparing and combining multiple metagenomic datasetsCode0
CommentWatcher: An Open Source Web-based platform for analyzing discussions on web forumsCode0
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial NetworksCode0
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