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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 27712780 of 9051 papers

TitleStatusHype
Facilitating bootstrapped and rarefaction-based microbiome diversity analysis with q2-bootsCode0
Batch Active Learning at ScaleCode0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related QueriesCode0
Hypergraph Clustering for Finding Diverse and Experienced GroupsCode0
Fairness and Diversity in Recommender Systems: A SurveyCode0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Measuring the Diversity of Automatic Image DescriptionsCode0
FaceCoresetNet: Differentiable Coresets for Face Set RecognitionCode0
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness RewardCode0
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