Results 1 to 10 of about 902,116 (154)

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression † [PDF]

Energies, 2018
Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech ...
Gregory D. Merkel, Richard J. Povinelli, Ronald H. Brown   +2 more
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The use of adversaries for optimal neural network training [PDF]

EPJ Web of Conferences, 2019
B-decay data from the Belle experiment at the KEKB collider have a substantial background from e+e- -h> qq¯ events. To suppress this we employ deep neural network algorithms. These provide improved signal from background discrimination. However, the deep
Hawthorne-Gonzalvez Anton, Sevior Martin
doaj   +4 more sources

Deep oscillatory neural network

Scientific Reports
We propose the Deep Oscillatory Neural Network (DONN), a brain-inspired network architecture that incorporates oscillatory dynamics into learning.
Nurani Rajagopal Rohan, C. Vigneswaran, Sayan Ghosh, Kishore Rajendran, A. Gaurav, V. Srinivasa Chakravarthy   +5 more
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Evolutional deep neural network [PDF]

Physical Review E, 2021
The notion of an Evolutional Deep Neural Network (EDNN) is introduced for the solution of partial differential equations (PDE). The parameters of the network are trained to represent the initial state of the system only, and are subsequently updated dynamically, without any further training, to provide an accurate prediction of the evolution of the PDE
Yifan Du, Tamer A. Zaki
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Orthogonal Deep Neural Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization.
Shuai Li, Kui Jia, Yuxin Wen, Tongliang Liu, Dacheng Tao   +4 more
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Tweaking Deep Neural Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Deep neural networks are trained so as to achieve a kind of the maximum overall accuracy through a learning process using given training data. Therefore, it is difficult to fix them to improve the accuracies of specific problematic classes or classes of interest that may be valuable to some users or applications.
Kim, Jinwook, Yoon, Heeyong, Kim, Min-Soo   +2 more
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A Deep Learning Approach to Predict the Flow Field and Thermal ‎Patterns of Nonencapsulated Phase Change Materials ‎Suspensions in an Enclosure‎ [PDF]

Journal of Applied and Computational Mechanics, 2022
The flow and heat transfer of a novel type of functional phase change nanofluids, nano-‎encapsulated phase change suspensions, is investigated in the present study using a deep neural ‎networks framework.
Mohammad Edalatifar, Mohammad Bagher Tavakoli, Farbod Setoudeh   +2 more
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Deep Polynomial Neural Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention ...
Chrysos, G.G., Moschoglou, S., Bouritsas, G., Deng, J., Panagakis, Y., Zafeiriou, S.   +5 more
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Deep Neural Networks and An Application in Health Sciences

Van Tıp Dergisi, 2021
INTRODUCTION: Because there is more than one hidden layer between the input and output layers in the neural network algorithm, it is called "Deep Neural Networks". In the study, the Deep Neural Networks algorithm; different input (number of layers, epoch,
Sadi Elasan
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Survey on Backdoor Attacks and Countermeasures in Deep Neural Network [PDF]

Jisuanji kexue yu tansuo, 2023
The neural network backdoor attack aims to implant a hidden backdoor into the deep neural network, so that the infected model behaves normally on benign test samples, but behaves abnormally on poisoned test samples with backdoor triggers.
QIAN Hanwei, SUN Weisong
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