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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 24712480 of 9051 papers

TitleStatusHype
GDPP: Learning Diverse Generations Using Determinantal Point ProcessCode0
Discovering the Elite Hypervolume by Leveraging Interspecies CorrelationCode0
Game Theory for Adversarial Attacks and DefensesCode0
3D2M Dataset: A 3-Dimension diverse Mesh DatasetCode0
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data GenerationCode0
Beyond Aesthetics: Cultural Competence in Text-to-Image ModelsCode0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language ModelsCode0
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-TrainingCode0
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