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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 20512060 of 9051 papers

TitleStatusHype
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration0
Repulsive Latent Score Distillation for Solving Inverse ProblemsCode0
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification0
Learning k-Determinantal Point Processes for Personalized Ranking0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning0
video-SALMONN: Speech-Enhanced Audio-Visual Large Language ModelsCode0
Unseen Object Reasoning with Shared Appearance CuesCode0
DEM: Distribution Edited Model for Training with Mixed Data Distributions0
STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionalsCode1
InternLM-Law: An Open Source Chinese Legal Large Language ModelCode1
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