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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 15811590 of 9051 papers

TitleStatusHype
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
Interpretable Long-term Action Quality AssessmentCode1
Inverse Materials Design by Large Language Model-Assisted Generative FrameworkCode1
LasHeR: A Large-scale High-diversity Benchmark for RGBT TrackingCode1
FreEformer: Frequency Enhanced Transformer for Multivariate Time Series ForecastingCode1
Mask as Supervision: Leveraging Unified Mask Information for Unsupervised 3D Pose EstimationCode1
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level AlignmentCode1
Style-Specific Neurons for Steering LLMs in Text Style TransferCode1
Online Damage Recovery for Physical Robots with Hierarchical Quality-DiversityCode1
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