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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 19511960 of 9051 papers

TitleStatusHype
Improved Generalization of Weight Space Networks via AugmentationsCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
Image Captioning via Dynamic Path CustomizationCode0
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightCode0
Aligning Sentence Simplification with ESL Learner's Proficiency for Language AcquisitionCode0
Cross-Part Learning for Fine-Grained Image ClassificationCode0
Exclusion of the fittest predicts microbial community diversity in fluctuating environmentsCode0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial NetworksCode0
Illuminating the Diversity-Fitness Trade-Off in Black-Box OptimizationCode0
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