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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 21512160 of 9051 papers

TitleStatusHype
Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail RecognitionCode0
Hierarchical Pruning of Deep Ensembles with Focal DiversityCode0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?Code0
Guylingo: The Republic of Guyana Creole CorporaCode0
CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous DrivingCode0
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickCode0
Harmony in Diversity: Merging Neural Networks with Canonical Correlation AnalysisCode0
Growing Artificial Neural Networks for Control: the Role of Neuronal DiversityCode0
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