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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 35913600 of 9051 papers

TitleStatusHype
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation LearningCode0
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model0
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Deformation Robust Text Spotting with Geometric Prior0
Learning Diverse Features in Vision Transformers for Improved GeneralizationCode0
Learning Vision-based Pursuit-Evasion Robot Policies0
Is the U.S. Legal System Ready for AI's Challenges to Human Values?0
Denoising Attention for Query-aware User Modeling in Personalized Search0
CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentation0
Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning0
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