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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

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Showing 961–970 of 9051 papers

TitleStatusHype
DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network—0
Acquisition of Recursive Possessives and Recursive Locatives in Mandarin—0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
Segmentation-based Extraction of Key Components from ECG Images: A Framework for Precise Classification and Digitization—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
A Review of the Marathi Natural Language Processing—0
Ethics and Technical Aspects of Generative AI Models in Digital Content Creation—0
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