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CQ ^+ Training: Minimizing Accuracy Loss in Conversion From Convolutional Neural Networks to Spiking Neural Networks
CQ ^+ Training: Minimizing Accuracy Loss in Conversion From Convolutional Neural Networks to Spiking Neural Networks
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CQ ^+ Training: Minimizing Accuracy Loss in Conversion From Convolutional Neural Networks to Spiking Neural Networks

Yan, Zhanglu ; Zhou, Jun ; Wong, Weng-Fai

IEEE transactions on pattern analysis and machine intelligence, 2023-10, Vol.45 (10), p.11600-11611 [Periódico revisado por pares]

United States: IEEE

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2
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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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

Kiranyaz, Serkan ; Avci, Onur ; Abdeljaber, Osama ; Ince, Turker ; Gabbouj, Moncef ; Inman, Daniel J.

Mechanical systems and signal processing, 2021-04, Vol.151, p.107398, Article 107398 [Periódico revisado por pares]

Berlin: Elsevier Ltd

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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)
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)

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Hydrology and earth system sciences, 2021-04, Vol.25 (3), p.1671-1687 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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5
HGNN+: General Hypergraph Neural Networks
HGNN+: General Hypergraph Neural Networks
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HGNN+: General Hypergraph Neural Networks

Gao, Yue ; Feng, Yifan ; Ji, Shuyi ; Ji, Rongrong

IEEE transactions on pattern analysis and machine intelligence, 2023-03, Vol.45 (3), p.3181-3199 [Periódico revisado por pares]

United States: IEEE

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6
Combination of Convolutional Neural Networks and Recurrent Neural Networks for predicting soil properties using Vis–NIR spectroscopy
Combination of Convolutional Neural Networks and Recurrent Neural Networks for predicting soil properties using Vis–NIR spectroscopy
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Combination of Convolutional Neural Networks and Recurrent Neural Networks for predicting soil properties using Vis–NIR spectroscopy

Yang, Jiechao ; Wang, Xuelei ; Wang, Ruihua ; Wang, Huanjie

Geoderma, 2020-12, Vol.380, p.114616, Article 114616 [Periódico revisado por pares]

Elsevier B.V

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7
A Comprehensive Survey on Graph Neural Networks
A Comprehensive Survey on Graph Neural Networks
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A Comprehensive Survey on Graph Neural Networks

Wu, Zonghan ; Pan, Shirui ; Chen, Fengwen ; Long, Guodong ; Zhang, Chengqi ; Yu, Philip S.

IEEE transaction on neural networks and learning systems, 2021-01, Vol.32 (1), p.4-24

United States: IEEE

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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

Wu, Nan ; Phang, Jason ; Park, Jungkyu ; Shen, Yiqiu ; Huang, Zhe ; Zorin, Masha ; Jastrzebski, Stanislaw ; Fevry, Thibault ; Katsnelson, Joe ; Kim, Eric ; Wolfson, Stacey ; Parikh, Ujas ; Gaddam, Sushma ; Lin, Leng Leng Young ; Ho, Kara ; Weinstein, Joshua D. ; Reig, Beatriu ; Gao, Yiming ; Toth, Hildegard ; Pysarenko, Kristine ; Lewin, Alana ; Lee, Jiyon ; Airola, Krystal ; Mema, Eralda ; Chung, Stephanie ; Hwang, Esther ; Samreen, Naziya ; Kim, S. Gene ; Heacock, Laura ; Moy, Linda ; Cho, Kyunghyun ; Geras, Krzysztof J.

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

United States: IEEE

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9
Deep learning in spiking neural networks
Deep learning in spiking neural networks
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Deep learning in spiking neural networks

Tavanaei, Amirhossein ; Ghodrati, Masoud ; Kheradpisheh, Saeed Reza ; Masquelier, Timothée ; Maida, Anthony

Neural networks, 2019-03, Vol.111, p.47-63 [Periódico revisado por pares]

United States: Elsevier Ltd

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10
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

Mengu, Deniz ; Luo, Yi ; Rivenson, Yair ; Ozcan, Aydogan

IEEE journal of selected topics in quantum electronics, 2020-01, Vol.26 (1), p.1-14 [Periódico revisado por pares]

United States: IEEE

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