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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 29913000 of 9051 papers

TitleStatusHype
Event Transition Planning for Open-ended Text GenerationCode0
Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone ProgrammingCode0
Evolutionary bagging for ensemble learningCode0
Evaluating Neural Language Models as Cognitive Models of Language AcquisitionCode0
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer LearningCode0
Evaluating Diversity in Automatic Poetry GenerationCode0
CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous DrivingCode0
PMT-IQA: Progressive Multi-task Learning for Blind Image Quality AssessmentCode0
Evaluating Fairness in Argument RetrievalCode0
Evaluating and Improving Graph to Text Generation with Large Language ModelsCode0
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