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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 3221–3230 of 9051 papers

TitleStatusHype
Steering Responsible AI: A Case for Algorithmic Pluralism—0
Workforce pDEI: Productivity Coupled with DEI—0
Modelling the Formation of Peer-to-Peer Trading Coalitions and Prosumer Participation Incentives in Transactive Energy Communities—0
Multi-Task Reinforcement Learning with Mixture of Orthogonal ExpertsCode1
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
Partially Randomizing Transformer Weights for Dialogue Response Diversity—0
Unsupervised Estimation of Ensemble Accuracy—0
Diverse Shape Completion via Style Modulated Generative Adversarial Networks—0
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images—0
An Empirical Bayes Framework for Open-Domain Dialogue Generation—0
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