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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 161170 of 9051 papers

TitleStatusHype
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
EDGE: Editable Dance Generation From MusicCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
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