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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 4191–4200 of 9051 papers

TitleStatusHype
Biased Random-Key Genetic Algorithms: A Review—0
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning—0
Discriminative Active Learning for Domain Adaptation—0
Bias, diversity, and challenges to fairness in classification and automated text analysis. From libraries to AI and back—0
An Energy Activity Dataset for Smart Homes—0
Discrete Structural Planning for Generating Diverse Translations—0
Bias Begets Bias: The Impact of Biased Embeddings on Diffusion Models—0
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Discrepancy-based Evolutionary Diversity Optimization—0
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