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1
Neural networks
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Neural networks

ISSN0893-6080

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2
Recurrent Neural Networks
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Recurrent Neural Networks

ISBN953-7619-08-7;ISBN953-51-5795-7

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3
Artificial Neural Networks and Neural Information Processing -- ICANN/ICONIP 2003: Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings
Artificial Neural Networks and Neural Information Processing -- ICANN/ICONIP 2003: Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings
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Artificial Neural Networks and Neural Information Processing -- ICANN/ICONIP 2003: Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings

Springer-Verlag Berlin Heidelberg 2003 ;ISSN: 0302-9743 ;ISBN: 3540404082 ;ISBN: 9783540404088 ;ISBN: 3662204614 ;ISBN: 9783662204610 ;EISSN: 1611-3349 ;EISBN: 9783540449898 ;EISBN: 3540449892 ;DOI: 10.1007/3-540-44989-2 ;OCLC: 958523112

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4
Optical Memory & Neural Networks
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Optical Memory & Neural Networks

ISSN1060-992X

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5
Artificial Neural Networks - Application
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Artificial Neural Networks - Application

ISBN953-307-188-5;ISBN953-51-4499-5

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6
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

Journal of hydrometeorology, 2019-12, Vol.20 (12), p.2273-2289 [Peer Reviewed Journal]

2019 American Meteorological Society ;Copyright American Meteorological Society Dec 2019 ;ISSN: 1525-755X ;EISSN: 1525-7541 ;DOI: 10.1175/jhm-d-19-0110.1

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7
1D convolutional neural networks and applications: A survey
1D convolutional neural networks and applications: A survey
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1D convolutional neural networks and applications: A survey

Mechanical systems and signal processing, 2021-04, Vol.151 [Peer Reviewed Journal]

ISSN: 0888-3270 ;ISSN: 1096-1216 ;EISSN: 1096-1216 ;DOI: 10.1016/j.ymssp.2020.107398

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8
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
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Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

Hydrology and earth system sciences, 2021-04, Vol.25 (3), p.1671-1687 [Peer Reviewed Journal]

COPYRIGHT 2021 Copernicus GmbH ;2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1607-7938 ;ISSN: 1027-5606 ;EISSN: 1607-7938 ;DOI: 10.5194/hess-25-1671-2021

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9
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
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Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

IEEE transactions on medical imaging, 2020-04, Vol.39 (4), p.1184-1194

ISSN: 0278-0062 ;EISSN: 1558-254X ;DOI: 10.1109/TMI.2019.2945514 ;CODEN: ITMID4

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10
Deep learning in spiking neural networks
Deep learning in spiking neural networks
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Deep learning in spiking neural networks

Neural networks, 2019-03, Vol.111, p.47-63 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0893-6080 ;EISSN: 1879-2782 ;DOI: 10.1016/j.neunet.2018.12.002

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11
Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks
Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks
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Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks

IEEE journal of selected topics in quantum electronics, 2020-01, Vol.26 (1), p.1-14 [Peer Reviewed Journal]

ISSN: 1077-260X ;EISSN: 1558-4542 ;DOI: 10.1109/JSTQE.2019.2921376 ;CODEN: IJSQEN

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12
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

Nature (London), 2017-02, Vol.542 (7639), p.115-118 [Peer Reviewed Journal]

COPYRIGHT 2017 Nature Publishing Group ;COPYRIGHT 2017 Nature Publishing Group ;Copyright Nature Publishing Group Feb 2, 2017 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/nature21056 ;PMID: 28117445 ;CODEN: NATUAS

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13
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks
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Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks

IEEE transaction on neural networks and learning systems, 2017-10, Vol.28 (10), p.2319-2333

ISSN: 2162-237X ;EISSN: 2162-2388 ;DOI: 10.1109/TNNLS.2016.2582512 ;PMID: 27429451 ;CODEN: ITNNAL

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14
Neural Networks for Power Flow : Graph Neural Solver
Neural Networks for Power Flow : Graph Neural Solver
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Article 		Add to My Research

Neural Networks for Power Flow : Graph Neural Solver

Electric power systems research, 2020-12, Vol.189 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0378-7796 ;EISSN: 1873-2046 ;DOI: 10.1016/j.epsr.2020.106547

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15
Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 10-13, 2013, Proceedings
Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 10-13, 2013, Proceedings
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Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 10-13, 2013, Proceedings

ISBN: 3642407285 ;ISBN: 9783642407284 ;ISBN: 3642407277 ;ISBN: 9783642407277 ;EISBN: 3642407285 ;EISBN: 9783642407284 ;OCLC: 936312750

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16
Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification
Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification
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Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification

IEEE transactions on geoscience and remote sensing, 2017-02, Vol.55, p.645-657 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0196-2892 ;EISSN: 1558-0644 ;DOI: 10.1109/tgrs.2016.2612821

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17
GraphDTA: predicting drug–target binding affinity with graph neural networks
GraphDTA: predicting drug–target binding affinity with graph neural networks
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GraphDTA: predicting drug–target binding affinity with graph neural networks

Bioinformatics, 2021-05, Vol.37 (8), p.1140-1147 [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2020 ;ISSN: 1367-4803 ;EISSN: 1460-2059 ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/btaa921

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18
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
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Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

Journal of fluid mechanics, 2020-01, Vol.882, Article A13 [Peer Reviewed Journal]

2019 Cambridge University Press ;ISSN: 0022-1120 ;EISSN: 1469-7645 ;DOI: 10.1017/jfm.2019.822

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19
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Journal of scientific computing, 2022-09, Vol.92 (3), p.88, Article 88 [Peer Reviewed Journal]

The Author(s) 2022 ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-7474 ;EISSN: 1573-7691 ;DOI: 10.1007/s10915-022-01939-z

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20
Hands-On Deep Learning with R: A Practical Guide to Designing, Building, and Improving Neural Network Models Using R
Hands-On Deep Learning with R: A Practical Guide to Designing, Building, and Improving Neural Network Models Using R
Material Type:
Book 		Add to My Research

Hands-On Deep Learning with R: A Practical Guide to Designing, Building, and Improving Neural Network Models Using R

ISBN: 9781788996839 ;ISBN: 1788996836 ;EISBN: 1788993780 ;EISBN: 9781788993784 ;OCLC: 1153089714

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