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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

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Showing 3711–3720 of 9051 papers

TitleStatusHype
Diverse Prompts: Illuminating the Prompt Space of Large Language Models with MAP-Elites—0
Angle Sensitive Pixels for Lensless Imaging on Spherical Sensors—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Finding Support Examples for In-Context Learning—0
Fine-grained Activities of People Worldwide—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—0
Controlling Character Motions without Observable Driving Source—0
ADLM -- stega: A Universal Adaptive Token Selection Algorithm for Improving Steganographic Text Quality via Information Entropy—0
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases—0
Generating Diverse Training Samples for Relation Extraction with Large Language Models—0
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