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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 19211930 of 9051 papers

TitleStatusHype
Improved Generation of Synthetic Imaging Data Using Feature-Aligned DiffusionCode0
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
Improved Generalization of Weight Space Networks via AugmentationsCode0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
Implementing Smart Contracts: The case of NFT-rental with pay-per-likeCode0
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brainCode0
Importance of Search and Evaluation Strategies in Neural Dialogue ModelingCode0
Activation Maximization Generative Adversarial NetsCode0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
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