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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 131140 of 9051 papers

TitleStatusHype
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
Diffusion Models Beat GANs on Image SynthesisCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Diffusion Bridge Implicit ModelsCode2
Test-time Alignment of Diffusion Models without Reward Over-optimizationCode2
Ambiguous Medical Image Segmentation using Diffusion ModelsCode2
gRNAde: Geometric Deep Learning for 3D RNA inverse designCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
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