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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 32813290 of 9051 papers

TitleStatusHype
Aligning Instruction Tuning with Pre-training0
Cross-Dataset Generalization in Deep Learning0
Cross-Cutting Political Awareness through Diverse News Recommendations0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling0
Critic Sequential Monte Carlo0
Audio-Visual Segmentation via Unlabeled Frame Exploitation0
AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion0
ADAGE: Active Defenses Against GNN Extraction0
A Categorized Reflection Removal Dataset with Diverse Real-world Scenes0
Creativity Has Left the Chat: The Price of Debiasing Language Models0
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