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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 35013510 of 9051 papers

TitleStatusHype
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT ImagesCode0
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning0
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation0
Multi-Static ISAC in Cell-Free Massive MIMO: Precoder Design and Privacy AssessmentCode1
Diversifying Question Generation over Knowledge Base via External Natural Questions0
M^3CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders0
A mirror-Unet architecture for PET/CT lesion segmentationCode0
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