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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 50715080 of 9051 papers

TitleStatusHype
Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics0
Robust Scheduling with GFlowNets0
Robust Visual Tracking via Statistical Positive Sample Generation and Gradient Aware Learning0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL0
Role of Structural and Conformational Diversity for Machine Learning Potentials0
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions0
Roles of repertoire diversity in robustness of humoral immune response0
RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation0
ROOTS: a toolkit for easy, fast and consistent processing of large sequential annotated data collections0
Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task0
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