Results 11 to 20 of about 5,846,406 (312)
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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Approximation in shift-invariant spaces with deep ReLU neural networks [PDF]
, 2022 We study the expressive power of deep ReLU neural networks for approximating functions in dilated shift-invariant spaces, which are widely used in signal processing, image processing, communications and so on.
Wang, Yang, Li, Zhen, Yang, Yunfei
core +1 more source
A 15-Gbps BiCMOS XNOR gate for fast recognition of COVID-19 in binarized neural networks [PDF]
, 2022 The COVID-19 pandemic is spreading around the world causing more than 177 million cases and over 3.8 million deaths according to the European Centre for Disease Prevention and Control.
W. Marar, Hazem +3 more
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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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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.
Jinwook Kim +2 more
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Topology of deep neural networks
J. Mach. Learn. Res., 2020 We study how the topology of a data set $M = M_a \cup M_b \subseteq \mathbb{R}^d$, representing two classes $a$ and $b$ in a binary classification problem, changes as it passes through the layers of a well-trained neural network, i.e., with perfect accuracy on training set and near-zero generalization error ($\approx 0.01\%$). The goal is to shed light
Gregory Naitzat +2 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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Imbedding Deep Neural Networks
CoRR, 2022 Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem.
Andrew Corbett, Dmitry Kangin
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