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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 16711680 of 9051 papers

TitleStatusHype
Auditing Google's Search Algorithm: Measuring News Diversity Across Brazil, the UK, and the US0
Aligning Instruction Tuning with Pre-training0
ADAGE: Active Defenses Against GNN Extraction0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling0
A Categorized Reflection Removal Dataset with Diverse Real-world Scenes0
Audio-Visual Segmentation via Unlabeled Frame Exploitation0
AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion0
ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio0
Curating Grounded Synthetic Data with Global Perspectives for Equitable AI0
Audio-to-Score Conversion Model Based on Whisper methodology0
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