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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 141150 of 9051 papers

TitleStatusHype
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
Diffusion Bridge Implicit ModelsCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
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