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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 20712080 of 9051 papers

TitleStatusHype
A Real-time Global Inference Network for One-stage Referring Expression ComprehensionCode0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
How Well Do LLMs Identify Cultural Unity in Diversity?Code0
How well do you know your summarization datasets?Code0
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped usersCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
Towards control of opinion diversity by introducing zealots into a polarised social groupCode0
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
ArchiGuesser -- AI Art Architecture Educational GameCode0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
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