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PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks
PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks
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PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

Sadeghi, Mojtaba ; Asanjan, Ata Akbari ; Faridzad, Mohammad ; Nguyen, Phu ; Hsu, Kuolin ; Sorooshian, Soroosh ; Braithwaite, Dan

Journal of hydrometeorology, 2019-12, Vol.20 (12), p.2273-2289 [Periódico revisado por pares]

Boston: American Meteorological Society

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Dermatologist-level classification of skin cancer with deep neural networks
Dermatologist-level classification of skin cancer with deep neural networks
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Dermatologist-level classification of skin cancer with deep neural networks

Esteva, Andre ; Kuprel, Brett ; Novoa, Roberto A ; Ko, Justin ; Swetter, Susan M ; Blau, Helen M ; Thrun, Sebastian

Nature (London), 2017-02, Vol.542 (7639), p.115-118 [Periódico revisado por pares]

England: Nature Publishing Group

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lemon: LEns MOdelling with Neural networks – I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks
lemon: LEns MOdelling with Neural networks – I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks
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lemon: LEns MOdelling with Neural networks – I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks

Gentile, Fabrizio ; Tortora, Crescenzo ; Covone, Giovanni ; Koopmans, Léon V E ; Li, Rui ; Leuzzi, Laura ; Napolitano, Nicola R

Monthly notices of the Royal Astronomical Society, 2023-05, Vol.522 (4), p.5442-5455 [Periódico revisado por pares]

Oxford University Press

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Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
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Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

Cha, Young‐Jin ; Choi, Wooram ; Büyüköztürk, Oral

Computer-aided civil and infrastructure engineering, 2017-05, Vol.32 (5), p.361-378 [Periódico revisado por pares]

Hoboken: Wiley Subscription Services, Inc

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5
Star–galaxy classification using deep convolutional neural networks
Star–galaxy classification using deep convolutional neural networks
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Star–galaxy classification using deep convolutional neural networks

Kim, Edward J ; Brunner, Robert J

Monthly notices of the Royal Astronomical Society, 2017-02, Vol.464 (4), p.4463-4463 [Periódico revisado por pares]

London: Oxford University Press

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Efficient training of physics‐informed neural networks via importance sampling
Efficient training of physics‐informed neural networks via importance sampling
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Efficient training of physics‐informed neural networks via importance sampling

Nabian, Mohammad Amin ; Gladstone, Rini Jasmine ; Meidani, Hadi

Computer-aided civil and infrastructure engineering, 2021-08, Vol.36 (8), p.962-977 [Periódico revisado por pares]

Hoboken: Wiley Subscription Services, Inc

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Identifying drug–target interactions based on graph convolutional network and deep neural network
Identifying drug–target interactions based on graph convolutional network and deep neural network
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Identifying drug–target interactions based on graph convolutional network and deep neural network

Zhao, Tianyi ; Hu, Yang ; Valsdottir, Linda R ; Zang, Tianyi ; Peng, Jiajie

Briefings in bioinformatics, 2021-03, Vol.22 (2), p.2141-2150 [Periódico revisado por pares]

England: Oxford University Press

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Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise
Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise
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Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise

Mehranian, Abolfazl ; Wollenweber, Scott D. ; Walker, Matthew D. ; Bradley, Kevin M. ; Fielding, Patrick A. ; Su, Kuan-Hao ; Johnsen, Robert ; Kotasidis, Fotis ; Jansen, Floris P. ; McGowan, Daniel R.

European journal of nuclear medicine and molecular imaging, 2022-01, Vol.49 (2), p.539-549 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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Amyloid-β-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks
Amyloid-β-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks
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Amyloid-β-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks

Mucke, Lennart ; Palop, Jorge J

Nature Neuroscience, 2010-07, Vol.13 (7), p.812-818 [Periódico revisado por pares]

United States: Nature Publishing Group

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Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network
Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network
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Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

Han, Seung Seog ; Park, Gyeong Hun ; Lim, Woohyung ; Kim, Myoung Shin ; Na, Jung Im ; Park, Ilwoo ; Chang, Sung Eun Sakakibara, Manabu

PloS one, 2018-01, Vol.13 (1), p.e0191493-e0191493 [Periódico revisado por pares]

United States: Public Library of Science

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