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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 11411150 of 9051 papers

TitleStatusHype
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image RestorationCode1
Scalable Diverse Model Selection for Accessible Transfer LearningCode1
AlphaGarden: Learning to Autonomously Tend a Polyculture GardenCode1
Towards Active Vision for Action Localization with Reactive Control and Predictive LearningCode1
Functional connectivity ensemble method to enhance BCI performance (FUCONE)Code1
Qimera: Data-free Quantization with Synthetic Boundary Supporting SamplesCode1
Improving Contrastive Learning on Imbalanced Seed Data via Open-World SamplingCode1
PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and ClassificationCode1
The chemical space of terpenes: insights from data science and AICode1
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