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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 47614770 of 9051 papers

TitleStatusHype
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance SamplingCode0
Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity0
TripleE: Easy Domain Generalization via Episodic ReplayCode0
Concise and interpretable multi-label rule setsCode0
Generative Category-Level Shape and Pose Estimation with Semantic PrimitivesCode1
Improving Sample Quality of Diffusion Models Using Self-Attention GuidanceCode7
GenDexGrasp: Generalizable Dexterous GraspingCode1
GFlowNets and variational inferenceCode0
Investigating Metric Diversity for Evaluating Long Document SummarisationCode0
Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge0
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