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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 3441–3450 of 9051 papers

TitleStatusHype
Continual Speaker Adaptation for Text-to-Speech Synthesis—0
A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions—0
AI Fairness for People with Disabilities: Point of View—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Continual Semantic Segmentation with Automatic Memory Sample Selection—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
A survey of part-of-speech tagging approaches applied to K’iche’—0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
Contextual Distillation Model for Diversified Recommendation—0
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