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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 23312340 of 9051 papers

TitleStatusHype
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing DataCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
Generating Natural Language Adversarial ExamplesCode0
Generating Neural Networks with Neural NetworksCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
Boosting Semantic Segmentation from the Perspective of Explicit Class EmbeddingsCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
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