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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 43814390 of 9051 papers

TitleStatusHype
Structured mutation inspired by evolutionary theory enriches population performance and diversity0
Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain DomainsCode0
Does Deep Active Learning Work in the Wild?0
Diverse legal case search0
Sustainable Diversity of Phage-Bacteria Systems0
ProtoSeg: Interpretable Semantic Segmentation with Prototypical PartsCode1
Informational Diversity and Affinity Bias in Team Growth Dynamics0
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?Code0
Joint Training of Deep Ensembles Fails Due to Learner CollusionCode0
Variation-Aware Semantic Image Synthesis0
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