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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 50115020 of 9051 papers

TitleStatusHype
Rethinking Image Cropping: Exploring Diverse Compositions From Global Views0
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach0
Rethinking Parameter Counting: Effective Dimensionality Revisited0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation0
RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY0
Rethinking the Evaluation of Unbiased Scene Graph Generation0
Rethinking Video Salient Object Ranking0
Retinal Vessel Segmentation via Neuron Programming0
Retinotopy Inspired Brain Encoding Model and the All-for-One Training Recipe0
Retrievability in an Integrated Retrieval System: An Extended Study0
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