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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 621630 of 9051 papers

TitleStatusHype
Distill Any Depth: Distillation Creates a Stronger Monocular Depth EstimatorCode4
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models0
SMT(LIA) Sampling with High Diversity0
Effect of Gender Fair Job Description on Generative AI Images0
MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment0
olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language ModelsCode11
Inverse Materials Design by Large Language Model-Assisted Generative FrameworkCode1
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
AIRIS2 : a Smart Gateway Diversity Algorithm for Very High-Throughput Satellite Systems0
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals0
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