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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 4551–4560 of 9051 papers

TitleStatusHype
Decomposed evaluations of geographic disparities in text-to-image models—0
Decomposition of the Leinster-Cobbold Diversity Index—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Decoupling Pragmatics: Discriminative Decoding for Referring Expression Generation—0
Decoupling Shape and Density for Liver Lesion Synthesis Using Conditional Generative Adversarial Networks—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Does Deep Active Learning Work in the Wild?—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Deep Active Learning in the Open World—0
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