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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 22412250 of 9051 papers

TitleStatusHype
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Hierarchical Pruning of Deep Ensembles with Focal DiversityCode0
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture ModelingCode0
A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic MeasuresCode0
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial NetworksCode0
GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANsCode0
Global Counterfactual DirectionsCode0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
GFlowNets and variational inferenceCode0
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