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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

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Showing 811–820 of 9051 papers

TitleStatusHype
GUARD: A Safe Reinforcement Learning BenchmarkCode1
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability AssessmentCode1
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion DiffusionCode1
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
Learning Diverse Risk Preferences in Population-based Self-playCode1
Learning In-context Learning for Named Entity RecognitionCode1
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
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