Results 1 to 10 of about 218,745 (167)
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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DEEP NEURAL NETWORKS APPLICATIONS IN THE STUDY OF A GEOLOGICAL INDICATOR [PDF]
Sustainable Extraction and Processing of Raw Materials Journal, 2021 . The differences between shallow neural networks and deep neural networks are considered. Data from operational exploration of an open pit mine are used to train different types of deep neural networks to predict a useful indicator.
Kremena Arsova–Borisova +1 more
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Deep Polynomial Neural Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). Code: https://github.com/grigorisg9gr/polynomial_nets.
Grigorios G. Chrysos +5 more
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Implementation of deep learning in drug design
MedComm – Future Medicine, 2022 The field of deep learning has witnessed dramatic and rapid progress in the past several years, largely driven by the availability of massive datasets and increased computational power.
Bo Yang, Kan Li, Xiuqin Zhong, Jun Zou
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Digital holographic microscopy applied to 3D computer microvision by using deep neural networks [PDF]
EPJ Web of Conferences, 2023 Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments.
Brito Carcaño Jesús E. +6 more
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IEEE Access, 2022 Due to the popularity of 5G connectivity and The Internet of Things sensors, deep learning algorithms are being extended to edge devices. Compared with AI(Artificial Intelligence) cloud platforms, the deployment of deep neural networks on edge devices ...
Lin He, Lijun Peng, Lile He
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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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Deep Neural Networks as Complex Networks
CoRR, 2022 Deep Neural Networks are, from a physical perspective, graphs whose `links` and `vertices` iteratively process data and solve tasks sub-optimally. We use Complex Network Theory (CNT) to represents Deep Neural Networks (DNNs) as directed weighted graphs: within this framework, we introduce metrics to study DNNs as dynamical systems, with a granularity ...
Emanuele La Malfa +4 more
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Deep Neural Networks for Network Routing [PDF]
2019 International Joint Conference on Neural Networks (IJCNN), 2019 In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g.
Reis, João +5 more
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A new deep neural network for forecasting: Deep dendritic artificial neural network
Artificial Intelligence Review, 2023 Abstract Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures.
Egrioglu, Erol, Bas, Eren
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