Showing 20 of 829 academic articles
Meng Lyu, Wei Wang, Hao Wang, Haichao Wang et al. • Scientific Reports
(2017)
In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep...
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DOI: https://doi.org/10.1038/s41598-017-18171-7
PMC: 5736587
Gustau Camps‐Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein • Wiley eBooks
(2021)
Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only re...
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Open Access
DOI: https://doi.org/10.1002/9781119646181
Jordan Guerguiev, Timothy Lillicrap, Blake A. Richards • eLife
(2017)
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep le...
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DOI: https://doi.org/10.7554/elife.22901
PMC: 5716677
Ge Wang, Jong Chul Ye, Bruno De Man • Nature Machine Intelligence
(2020)
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DOI: https://doi.org/10.1038/s42256-020-00273-z
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier et al. • Neural Networks
(2018)
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Open Access
DOI: https://doi.org/10.1016/j.neunet.2018.12.002
Jinjiang Wang, Yulin Ma, Laibin Zhang, Robert X. Gao et al. • Journal of Manufacturing Systems
(2018)
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Open Access
DOI: https://doi.org/10.1016/j.jmsy.2018.01.003
Shutao Li A, Weiwei Song, Leyuan Fang, Yushi Chen et al. • IEEE Transactions on Geoscience and Remote Sensing
(2019)
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine ...
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Open Access
DOI: https://doi.org/10.1109/tgrs.2019.2907932
Chaoyun Zhang, Paul Patras, Hamed Haddadi • IEEE Communications Surveys & Tutorials
(2019)
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traff...
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Open Access
DOI: https://doi.org/10.1109/comst.2019.2904897
Ruud J. G. van Sloun, Regev Cohen, Yonina C. Eldar • Proceedings of the IEEE
(2019)
In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on ...
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Open Access
DOI: https://doi.org/10.1109/jproc.2019.2932116
Wei Ma, Zhaocheng Liu, Zhaxylyk A. Kudyshev, Alexandra E. Boltasseva et al. • Nature Photonics
(2020)
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DOI: https://doi.org/10.1038/s41566-020-0685-y
Chao Zuo, Jiaming Qian, Shijie Feng, Wei Yin et al. • Light Science & Applications
(2022)
With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, n...
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DOI: https://doi.org/10.1038/s41377-022-00714-x
PMC: 8866517
Kalaivani Sundararajan, Damon L. Woodard • ACM Computing Surveys
(2018)
In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate ...
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DOI: https://doi.org/10.1145/3190618
Andreas Kamilaris, Francesc Xavier Prenafeta-Boldú • Computers and Electronics in Agriculture
(2018)
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Open Access
DOI: https://doi.org/10.1016/j.compag.2018.02.016
Steven L. Brunton (2020)
This video gives an overview of how Machine Learning is being used in Fluid Mechanics. In fact, fluid mechanics is one of the original 'big data' sciences, and many advances in ML came out of fluids....
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DOI: https://doi.org/10.52843/cassyni.27tbdb
Mortaza Aghbashlo, Wanxi Peng, Meisam Tabatabaei, Soteris A. Kalogirou et al. • Progress in Energy and Combustion Science
(2021)
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Open Access
DOI: https://doi.org/10.1016/j.pecs.2021.100904
Jichao Li, Xiaosong Du, Joaquim R. R. A. Martins • Progress in Aerospace Sciences
(2022)
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and pr...
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DOI: https://doi.org/10.1016/j.paerosci.2022.100849
Lang Rao, Yuan Yuan, Xi Shen, Guocan Yu et al. • Nature Nanotechnology
(2024)
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DOI: https://doi.org/10.1038/s41565-024-01753-8
Haldun Aytuğ, Siddhartha Bhattacharyya, G.J. Koehler, Jane L. Snowdon • IEEE Transactions on Engineering Management
(1994)
This paper has two primary purposes: to motivate the need for machine learning in scheduling systems and to survey work on machine learning in scheduling. In order to motivate the need for machine learning in scheduling, we briefly motivate...
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DOI: https://doi.org/10.1109/17.293383
Hongyu Liu, Bo Lang • Applied Sciences
(2019)
Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in th...
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DOI: https://doi.org/10.3390/app9204396
Jean‐François Masson, John Simeon Biggins, Emilie Ringe • Nature Nanotechnology
(2023)
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public-domain
DOI: https://doi.org/10.1038/s41565-022-01284-0