SOTAVerified

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
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
Diverse Preference OptimizationCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
gRNAde: Geometric Deep Learning for 3D RNA inverse designCode2
Diffusion Bridge Implicit ModelsCode2
Diffusion Models Beat GANs on Image SynthesisCode2
Show:102550
← PrevPage 14 of 906Next →

No leaderboard results yet.