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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 12811290 of 9051 papers

TitleStatusHype
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
GIQA: Generated Image Quality AssessmentCode1
Global Tensor Motion PlanningCode1
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive TestingCode1
G-Eval: NLG Evaluation using GPT-4 with Better Human AlignmentCode1
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
Greedy Bayesian Posterior Approximation with Deep EnsemblesCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
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