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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

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Showing 1781–1790 of 9051 papers

TitleStatusHype
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
Active Learning on Synthons for Molecular Design—0
Abnormal Event Detection In Videos Using Deep Embedding—0
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces—0
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy—0
Data Quality in Imitation Learning—0
Data Representation and Compression Using Linear-Programming Approximations—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
Artificial Intelligence Development Races in Heterogeneous Settings—0
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