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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 3351–3360 of 9051 papers

TitleStatusHype
Attention-based Ensemble for Deep Metric Learning—0
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition—0
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition—0
CORD: Generalizable Cooperation via Role Diversity—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling—0
Attempt Towards Stress Transfer in Speech-to-Speech Machine Translation—0
Coordinated Spectral Efficiency Prediction for Real-World 5G CoMP Systems—0
Coordinated Exploration in Concurrent Reinforcement Learning—0
Attacking Transformers with Feature Diversity Adversarial Perturbation—0
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