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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 19111920 of 9051 papers

TitleStatusHype
Improved Generalization of Weight Space Networks via AugmentationsCode0
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine LearningCode0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Improved Generation of Synthetic Imaging Data Using Feature-Aligned DiffusionCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brainCode0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
Implementing Smart Contracts: The case of NFT-rental with pay-per-likeCode0
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