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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 5681–5690 of 9051 papers

TitleStatusHype
The Influence of Local Search over Genetic Algorithms with Balanced Representations—0
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity—0
The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion—0
The Lexical Gap: An Improved Measure of Automated Image Description Quality—0
The Lock-in Hypothesis: Stagnation by Algorithm—0
The long-term impact of ranking algorithms in growing networks—0
The LTRC Hindi-Telugu Parallel Corpus—0
The many faces of deep learning—0
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes—0
The Marine Debris Forward-Looking Sonar Datasets—0
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