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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 19711980 of 9051 papers

TitleStatusHype
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
Mitigating Sybils in Federated Learning PoisoningCode0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
Illumination Spectrum Estimation for Multispectral Images via Surface Reflectance Modeling and Spatial-Spectral Feature GenerationCode0
Image Captioning via Dynamic Path CustomizationCode0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in TransformersCode0
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian LanguagesCode0
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