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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 14411450 of 9051 papers

TitleStatusHype
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Monocular Human-Object Reconstruction in the WildCode1
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story GenerationCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Color Space Learning for Cross-Color Person Re-IdentificationCode1
Multi-head Attention-based Deep Multiple Instance LearningCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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