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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 871880 of 9051 papers

TitleStatusHype
Entropy Minimization vs. Diversity Maximization for Domain AdaptationCode1
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text ClassificationCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefenseCode1
Differentiable Quality DiversityCode1
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language ModelCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Active learning for medical image segmentation with stochastic batchesCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
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