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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 55115520 of 9051 papers

TitleStatusHype
SVP: Style-Enhanced Vivid Portrait Talking Head Diffusion Model0
SWAG: A Wrapper Method for Sparse Learning0
SwaQuAD-24: QA Benchmark Dataset in Swahili0
Swarm Intelligence Enhanced Reasoning: A Density-Driven Framework for LLM-Based Multi-Agent Optimization0
Swarm Systems as a Platform for Open-Ended Evolutionary Dynamics0
SwipeCut: Interactive Segmentation with Diversified Seed Proposals0
Switch-BERT: Learning to Model Multimodal Interactions by Switching Attention and Input0
Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks0
Symmetric and Asymmetric Tendencies in Stable Complex Systems0
Symmetry-Breaking Augmentations for Ad Hoc Teamwork0
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