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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 31813190 of 9051 papers

TitleStatusHype
Customizing an Adversarial Example Generator with Class-Conditional GANs0
Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning0
Customized Image Narrative Generation via Interactive Visual Question Generation and Answering0
Autobots@LT-EDI-EACL2021: One World, One Family: Hope Speech Detection with BERT Transformer Model0
Curse of "Low" Dimensionality in Recommender Systems0
Curriculum Learning with Diversity for Supervised Computer Vision Tasks0
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment0
A User-Centered Investigation of Personal Music Tours0
Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data0
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks0
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