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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 29012910 of 9051 papers

TitleStatusHype
Active Generation Network of Human Skeleton for Action Recognition0
Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization0
Embracing Language Inclusivity and Diversity in CLIP through Continual Language LearningCode0
Analysis of Knowledge Tracing performance on synthesised student data0
NNOSE: Nearest Neighbor Occupational Skill ExtractionCode0
KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants0
Contributing Dimension Structure of Deep Feature for Coreset SelectionCode0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect0
Prompting Diverse Ideas: Increasing AI Idea Variance0
A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions0
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