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Refinado por: assunto: Mathematics remover assunto: Physical Sciences remover
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Convolutional neural network architectures for predicting DNA-protein binding
Convolutional neural network architectures for predicting DNA-protein binding
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Convolutional neural network architectures for predicting DNA-protein binding

Zeng, Haoyang ; Edwards, Matthew D ; Liu, Ge ; Gifford, David K

Bioinformatics, 2016-06, Vol.32 (12), p.i121-i127 [Periódico revisado por pares]

England: Oxford University Press

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2
PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities
PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities
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PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities

Kumar, Prabhat ; Kumar, Randhir ; Srivastava, Gautam ; Gupta, Govind P. ; Tripathi, Rakesh ; Gadekallu, Thippa Reddy ; Xiong, Neal N.

IEEE transactions on network science and engineering, 2021-07, Vol.8 (3), p.2326-2341 [Periódico revisado por pares]

Piscataway: IEEE

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3
AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction
AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction
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AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction

Chen, Jiamin ; Gao, Jianliang ; Lyu, Tengfei ; Oloulade, Babatounde Moctard ; Hu, Xiaohua

IEEE/ACM transactions on computational biology and bioinformatics, 2023-11, Vol.20 (6), p.3430-3439 [Periódico revisado por pares]

United States: IEEE

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4
The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve
The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve
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The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve

Mei, Song ; Montanari, Andrea

Communications on pure and applied mathematics, 2022-04, Vol.75 (4), p.667-766 [Periódico revisado por pares]

Melbourne: John Wiley & Sons Australia, Ltd

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5
NONPARAMETRIC REGRESSION USING DEEP NEURAL NETWORKS WITH RELU ACTIVATION FUNCTION
NONPARAMETRIC REGRESSION USING DEEP NEURAL NETWORKS WITH RELU ACTIVATION FUNCTION
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NONPARAMETRIC REGRESSION USING DEEP NEURAL NETWORKS WITH RELU ACTIVATION FUNCTION

Schmidt-Hieber, Johannes

The Annals of statistics, 2020-08, Vol.48 (4), p.1875-1897 [Periódico revisado por pares]

Hayward: Institute of Mathematical Statistics

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6
Parallel point-multiplication architecture using combined group operations for high-speed cryptographic applications
Parallel point-multiplication architecture using combined group operations for high-speed cryptographic applications
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Parallel point-multiplication architecture using combined group operations for high-speed cryptographic applications

Hossain, Md Selim ; Saeedi, Ehsan ; Kong, Yinan Choo, Kim-Kwang Raymond

PloS one, 2017-05, Vol.12 (5), p.e0176214-e0176214 [Periódico revisado por pares]

United States: Public Library of Science

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7
Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture
Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture
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Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture

Peng, Lihong ; Wang, Chang ; Tian, Xiongfei ; Zhou, Liqian ; Li, Keqin

IEEE/ACM transactions on computational biology and bioinformatics, 2022-11, Vol.19 (6), p.3456-3468 [Periódico revisado por pares]

United States: IEEE

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8
The unreasonable effectiveness of deep learning in artificial intelligence
The unreasonable effectiveness of deep learning in artificial intelligence
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The unreasonable effectiveness of deep learning in artificial intelligence

Sejnowski, Terrence J.

Proceedings of the National Academy of Sciences - PNAS, 2020-12, Vol.117 (48), p.30033-30038 [Periódico revisado por pares]

United States: National Academy of Sciences

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9
Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture
Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture
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Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture

Zhao, Zhenni ; Gong, Xinqi

IEEE/ACM transactions on computational biology and bioinformatics, 2019-09, Vol.16 (5), p.1753-1759 [Periódico revisado por pares]

United States: IEEE

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10
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
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Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

Zhang, Ruiyang ; Liu, Yang ; Sun, Hao

Computer methods in applied mechanics and engineering, 2020-09, Vol.369, p.113226, Article 113226 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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