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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 28512860 of 9051 papers

TitleStatusHype
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Capturing the diversity of multilingual societiesCode0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation NetworksCode0
Exploring the Performance-Reproducibility Trade-off in Quality-DiversityCode0
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental ComparisonsCode0
Exploring the Evolution of GANs through Quality DiversityCode0
Exploring the Role of Diversity in Example Selection for In-Context LearningCode0
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