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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 3021–3030 of 9051 papers

TitleStatusHype
greylock: A Python Package for Measuring The Composition of Complex DatasetsCode0
Informative Rays Selection for Few-Shot Neural Radiance Fields—0
Hybrid quantum cycle generative adversarial network for small molecule generation—0
Replica Tree-based Federated Learning using Limited DataCode0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
T cell receptor binding prediction: A machine learning revolution—0
A proposed new metric for the conceptual diversity of a text—0
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation—0
SVGDreamer: Text Guided SVG Generation with Diffusion ModelCode2
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