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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 35613570 of 9051 papers

TitleStatusHype
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning0
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining0
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
Sparse Codesigned Communication and Radar Systems0
Unsupervised Multi-document Summarization with Holistic Inference0
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers0
Score-PA: Score-based 3D Part AssemblyCode1
Behind Recommender Systems: the Geography of the ACM RecSys CommunityCode0
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation0
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
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