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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 231240 of 9051 papers

TitleStatusHype
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
ProtComposer: Compositional Protein Structure Generation with 3D EllipsoidsCode2
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based PoliciesCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
ReMoDiffuse: Retrieval-Augmented Motion Diffusion ModelCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
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