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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 4741–4750 of 9051 papers

TitleStatusHype
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation—0
Privacy-Aware Crowd Labelling for Machine Learning Tasks—0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator—0
Privacy protection based on mask template—0
Private Synthetic Data Meets Ensemble Learning—0
Private Training Set Inspection in MLaaS—0
Probabilistic Fixed Ballot Rules and Hybrid Domains—0
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries—0
Probabilistic Inference for Learning from Untrusted Sources—0
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
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