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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 291300 of 9051 papers

TitleStatusHype
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Find Any Part in 3DCode2
FLatten Transformer: Vision Transformer using Focused Linear AttentionCode2
Deep Rectangling for Image Stitching: A Learning BaselineCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Diffusion Models Beat GANs on Image SynthesisCode2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
Multi-Space Alignments Towards Universal LiDAR SegmentationCode2
General Scene Adaptation for Vision-and-Language NavigationCode2
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAXCode2
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