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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 21812190 of 9051 papers

TitleStatusHype
Quality-Diversity with Limited ResourcesCode0
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term ModelingCode2
Beyond Diagonal RIS-Aided Networks: Performance Analysis and Sectorization Tradeoff0
Diversity in Evolutionary Dynamics0
Polyhedral Conic Classifier for CTR Prediction0
Data Measurements for Decentralized Data Markets0
Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual AwarenessCode0
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract DescriptionsCode0
Promoting Fairness and Diversity in Speech Datasets for Mental Health and Neurological Disorders ResearchCode0
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