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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 12511260 of 9051 papers

TitleStatusHype
Diversidade linguística e inclusão digital: desafios para uma ia brasileira0
The Role of Domain Randomization in Training Diffusion Policies for Whole-Body Humanoid Control0
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions0
Designing a Robust Radiology Report Generation System0
PRIMO: Progressive Induction for Multi-hop Open Rule Generation0
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks0
α-TCVAE: On the relationship between Disentanglement and Diversity0
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language ModelsCode0
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