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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 4861–4870 of 9051 papers

TitleStatusHype
R3 Adversarial Network for Cross Model Face Recognition—0
RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts—0
Rainy screens: Collecting rainy datasets, indoors—0
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios—0
Random Features for Compositional Kernels—0
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures—0
Randomized heuristic repair for large-scale multidimensional knapsack problem—0
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