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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 29112920 of 9051 papers

TitleStatusHype
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Exploiting ConvNet Diversity for Flooding IdentificationCode0
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity SamplingCode0
Adversarial Multi-lingual Neural Relation ExtractionCode0
Exploratory State Representation LearningCode0
A Workbench for Autograding Retrieve/Generate SystemsCode0
Deep Co-Training for Semi-Supervised Image SegmentationCode0
Explaining crime diversity with Google street viewCode0
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible TemplatesCode0
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial DiversityCode0
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