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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 1991–2000 of 9051 papers

TitleStatusHype
Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions—0
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network—0
Designing a Robust Radiology Report Generation System—0
Cultivating DNN Diversity for Large Scale Video Labelling—0
Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation—0
Semantic and Expressive Variation in Image Captions Across Languages—0
A Spiking Neuron Synaptic Plasticity Model Optimized for Unsupervised Learning—0
Cultural Diversity and Its Impact on Governance—0
Cultural Evaluations of Vision-Language Models Have a Lot to Learn from Cultural Theory—0
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration—0
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