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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 38013810 of 9051 papers

TitleStatusHype
Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective0
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection0
Exploration by Random Reward Perturbation0
Compressed Sensing with Approximate Priors via Conditional Resampling0
Free-Space Optical MISO Communications With an Array of Detectors0
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation0
A Simple Background Augmentation Method for Object Detection with Diffusion Model0
Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms0
Explorable Tone Mapping Operators0
Exploiting Web Images for Weakly Supervised Object Detection0
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