Bayesian Neural Networks for Reversible Steganography by Ching-Chun Chang

“…A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks. However, non-trivial errors exist in inferences about some out-of-distribution and noisy data. …”
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Convolutional Neural Networks for Biometrics Applications by Jin Jiani

“…A convolutional neural network (CNN) is a feed-forward neural network that can react with other units in a specific range and can handle huge images well as a deep learning algorithm. …”
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Expert Neural Esimator For Control Applications by N Selvanathan, Mashuri Yaacob

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Optimizing Neural Networks for Imbalanced Data by I. de Zarzà, J. de Curtò, Carlos T. Calafate

Subjects: “…neural networks…”
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Neural Correlates of Craving in Methamphetamine Abuse by Fanak Shahmohammadi, Mehrshad Golesorkhi, Mohammad Mansour Riahi Kashani, Mehrdad Sangi, Ahmad Yoonessi, Ali Yoonessi

“…Conclusion: This study presented a novel and noninvasive method based on neural correlates to discriminate healthy individuals from methamphetamine drug abusers. …”
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A Review of Optical Neural Networks by Danni Zhang, Zhongwei Tan

Subjects: “…optical neural network…”
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Cytokines profile in pure neural leprosy by Izabela Jardim R. Pitta, Izabela Jardim R. Pitta, Debora Bartzen Moraes Angst, Roberta Olmo Pinheiro, Joyce Soares da Silva Vieira, Clarissa Neves Spitz, Clarissa Neves Spitz, Clarissa Neves Spitz, Ligia Rocha Andrade, Ligia Rocha Andrade, Ligia Rocha Andrade, Larissa Bittencourt Carvalho, Larissa Bittencourt Carvalho, Larissa Bittencourt Carvalho, Mariana Andrea Hacker, Euzenir Nunes Sarno, Marcia Rodrigues Jardim, Marcia Rodrigues Jardim, Marcia Rodrigues Jardim

Subjects: “…pure neural leprosy…”
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On conductance-based neural field models by Dimitris ePinotsis, Marco eLeite, Karl eFriston

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Holographic method of neural network design by Yakasova Natalya

“…Such conditions generate a new neural network, which is trained on examples of all classes, including the new class. …”
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Optogenetic feedback control of neural activity by Jonathan P Newman, Ming-fai Fong, Daniel C Millard, Clarissa J Whitmire, Garrett B Stanley, Steve M Potter

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EANN: Energy Adaptive Neural Networks by Salma Hassan, Sameh Attia, Khaled Nabil Salama, Hassan Mostafa

“…This paper proposes an Energy Adaptive Feedforward Neural Network (EANN). It uses multiple approximation techniques in the hardware implementation of the neuron unit. …”
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Micro-Neural Surgery (Third Part) by Ashok Govila

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The balance property in neural network modelling by Mario V. Wüthrich

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Assessing Efficiency in Artificial Neural Networks by Nicholas J. Schaub, Nathan Hotaling

Subjects: “…neural networks…”
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Balanced Neural Architecture and the Idling Brain by Brent eDoiron, Brent eDoiron, Ashok eLitwin-Kumar, Ashok eLitwin-Kumar

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Training Optimization for Artificial Neural Networks by Primitivo Toribio Luna, Roberto Alejo Eleuterio, Rosa María Valdovinos Rosas, Benjamín Gonzalo Rodríguez Méndez

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Inference Using Simulated Neural Moments by Michael Creel

Subjects: “…neural networks…”
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Energy efficiency and coding of neural network by Shengnan Li, Chuankui Yan, Ying Liu

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Survey on categorical data for neural networks by John T. Hancock, Taghi M. Khoshgoftaar

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The geometry of robustness in spiking neural networks by Nuno Calaim, Florian A Dehmelt, Pedro J Gonçalves, Christian K Machens

Subjects: “…spiking neural networks…”
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