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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 42014210 of 9051 papers

TitleStatusHype
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving0
Discrepancy-based Evolutionary Diversity Optimization0
Image Processing in Floriculture Using a robotic Mobile Platform0
Discovery data topology with the closure structure. Theoretical and practical aspects0
Discovering Unsupervised Behaviours from Full-State Trajectories0
Bias and Diversity in Synthetic-based Face Recognition0
Image Matching: An Application-oriented Benchmark0
Image Pre-processing on NumtaDB for Bengali Handwritten Digit Recognition0
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization0
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality0
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