Results 11 to 20 of about 1,035,049 (261)
Synthetic data production for biomedical research [PDF]
Osong Public Health and Research PerspectivesSynthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information. Although synthetic data does not consist of actual data points, it is
Yun Gyeong Lee +5 more
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Synthetic data, synthetic trust: navigating data challenges in the digital revolution [PDF]
The Lancet Digital HealthIn the evolving landscape of artificial intelligence (AI), the assumption that more data lead to better models has driven unchecked reliance on synthetic data to augment training datasets. Although synthetic data address crucial shortages of real-world training data, their overuse might propagate biases, accelerate model degradation, and compromise ...
Arman Koul +2 more
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Business Information ReviewSynthetic data generated using logistic, linear or exponential relationships with categorical variables with an element of multi-collinearity
Jimmy Nassif, Joe Tekli, Marc Kamradt
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CoRR, 2023 Synthetic data are becoming a critical tool for building artificially intelligent systems. Simulators provide a way of generating data systematically and at scale. These data can then be used either exclusively, or in conjunction with real data, for training and testing systems. Synthetic data are particularly attractive in cases where the availability
Daniel McDuff +2 more
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CoRR, 2023 This study leverages synthetic data as a validation set to reduce overfitting and ease the selection of the best model in AI development. While synthetic data have been used for augmenting the training set, we find that synthetic data can also significantly diversify the validation set, offering marked advantages in domains like healthcare, where data ...
Qixin Hu, Alan L. Yuille, Zongwei Zhou
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Synthetic data, real errors: how (not) to publish and use synthetic data
CoRR, 2023 Proceedings of the 40th International Conference on Machine Learning (ICML 2023)
Boris van Breugel +2 more
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Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
Applied Sciences, 2021 Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of healthcare data for secondary purposes. It is considered a safe approach for the sharing of sensitive data as it generates an artificial dataset that contains no ...
Fida K. Dankar, Mahmoud Ibrahim
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Synthetic Data for Model Selection
CoRR, 2021 Recent breakthroughs in synthetic data generation approaches made it possible to produce highly photorealistic images which are hardly distinguishable from real ones. Furthermore, synthetic generation pipelines have the potential to generate an unlimited number of images.
Matan Fintz +4 more
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Synthetic data for reef modelling
Ecological Informatics, 2023 Synthetic data mimics the statistical properties of real-world datasets while removing reference to sensitive or confidential information in the original dataset (Quintana, 2020). Synthetic data is also useful for general model testing and development, with many methods available for generating data from machine learning models (Raghunathan, 2021 ...
Rose Crocker +4 more
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Predictive design of sigma factor-specific promoters
Nature Communications, 2020 Automated design tools and tailored subunits are beneficial in fine-tuning all components of a complex genetic circuit. Here the authors create E. coli and B.
Maarten Van Brempt +6 more
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