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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 26912700 of 9051 papers

TitleStatusHype
Few-shot Image Generation via Masked DiscriminationCode0
Diversity in deep generative models and generative AICode0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
Federated Neural Topic ModelsCode0
Federated Stain Normalization for Computational PathologyCode0
Feature Space Particle Inference for Neural Network EnsemblesCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
Diversity Aware Relevance Learning for Argument SearchCode0
Federated Visual Classification with Real-World Data DistributionCode0
Few-shot Quality-Diversity OptimizationCode0
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