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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 571580 of 9051 papers

TitleStatusHype
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
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