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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 23612370 of 9051 papers

TitleStatusHype
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization0
Topological conditions drive stability in meta-ecosystems0
AFEN: Respiratory Disease Classification using Ensemble Learning0
Pedestrian Attribute Recognition as Label-balanced Multi-label LearningCode1
Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole LanguagesCode0
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse LandscapesCode3
Towards Geographic Inclusion in the Evaluation of Text-to-Image ModelsCode1
The Curse of Diversity in Ensemble-Based ExplorationCode0
Navigating Chemical Space with Latent FlowsCode1
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