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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 37413750 of 9051 papers

TitleStatusHype
Accurate RSS-Based Localization Using an Opposition-Based Learning Simulated Annealing Algorithm0
Alleviating the Long-Tail Problem in Conversational Recommender SystemsCode0
OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?0
Offline Diversity Maximization Under Imitation Constraints0
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent SpaceCode1
Contributions of El Niño Southern Oscillation (ENSO) Diversity to Low-Frequency Changes in ENSO VarianceCode0
Diffusion Models for Probabilistic Deconvolution of Galaxy ImagesCode0
Challenges and Solutions in AI for All0
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTVCode1
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?0
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