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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 23412350 of 9051 papers

TitleStatusHype
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity MetricsCode0
AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image SegmentationCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Boosting Deep Ensemble Performance with Hierarchical PruningCode0
Generating Language Corrections for Teaching Physical Control TasksCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
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