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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 19411950 of 9051 papers

TitleStatusHype
Auditing for Diversity using Representative ExamplesCode0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brainCode0
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightCode0
Illumination Spectrum Estimation for Multispectral Images via Surface Reflectance Modeling and Spatial-Spectral Feature GenerationCode0
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial NetworksCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
Illuminating the Diversity-Fitness Trade-Off in Black-Box OptimizationCode0
Image Captioning via Dynamic Path CustomizationCode0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
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