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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 27112720 of 9051 papers

TitleStatusHype
Federated Neural Topic ModelsCode0
Diversity By Design: Leveraging Distribution Matching for Offline Model-Based OptimizationCode0
Bayesian Renewables Scenario Generation via Deep Generative NetworksCode0
Federated Stain Normalization for Computational PathologyCode0
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parametersCode0
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image ClassificationCode0
Diversity-Driven Combination for Grammatical Error CorrectionCode0
Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookupCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
Federated Visual Classification with Real-World Data DistributionCode0
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