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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 21612170 of 9051 papers

TitleStatusHype
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designCode0
Guylingo: The Republic of Guyana Creole CorporaCode0
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
Adversarial Transformation Networks: Learning to Generate Adversarial ExamplesCode0
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and DiversityCode0
Hierarchical Pruning of Deep Ensembles with Focal DiversityCode0
Grouping Words with Semantic DiversityCode0
Group Relative Policy Optimization for Image CaptioningCode0
A Patch-Based Algorithm for Diverse and High Fidelity Single Image GenerationCode0
Channel Augmented Joint Learning for Visible-Infrared RecognitionCode0
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