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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 54515460 of 9051 papers

TitleStatusHype
Structural Compression of Convolutional Neural Networks0
Structural Consistency and Controllability for Diverse Colorization0
Structural Learning of Diverse Ranking0
Structured Deep Neural Network Pruning via Matrix Pivoting0
Structured Determinantal Point Processes0
Structured mutation inspired by evolutionary theory enriches population performance and diversity0
Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction0
Student's t-Generative Adversarial Networks0
Studying and Mitigating Biases in Sign Language Understanding Models0
Studying evolution of the primary body axis in vivo and in vitro0
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