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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 4031–4040 of 9051 papers

TitleStatusHype
Diverse Group Formation Based on Multiple Demographic Features—0
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates—0
Blind Motion Deblurring through SinGAN Architecture—0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
A new system for evaluating brand importance: A use case from the fashion industry—0
Diverse Exploration for Fast and Safe Policy Improvement—0
A News Recommender System Considering Temporal Dynamics and Diversity—0
Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators—0
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