Results 41 to 50 of about 6,478,030 (244)
A Dynamic Rectified Linear Activation Units
IEEE Access, 2019 Deep neural network regression models produce substantial gains in big data prediction systems. Multilayer perceptron neural (MPL) networks have more various properties than single-layer feedforward neural networks.
Xiaobin Hu +3 more
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On variation of gradients of deep neural networks
CoRR, 2018 30 pages, 6 tables, 2 ...
Yongdai Kim, Dongha Kim
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Backward Gradient Normalization in Deep Neural Networks
CoRR, 2021 We introduce a new technique for gradient normalization during neural network training. The gradients are rescaled during the backward pass using normalization layers introduced at certain points within the network architecture. These normalization nodes do not affect forward activity propagation, but modify backpropagation equations to permit a well ...
Alejandro Cabaña +1 more
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IEEE Access, 2020 Dynamic complex-valued matrix inversion is often used in the field of mathematics and engineering. Over the past years, many recurrent neural network models have been designed and researched to analyse the process of matrix inversion without noises ...
Sai Liu, Haiyan Tan, Lin Xiao
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Distributed computing methodology for training neural networks in an image-guided diagnostic application [PDF]
, 2006 Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of ...
Plagianakos, V.P. +2 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe main difficulty in using artificial neural networks, which are designed for classification, to detect a rare subpixel target in hyperspectral imaging is that there is typically only one actual target pixel available for training the neural networks ...
Edisanter Lo, Damien B. Josset
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Neural network-based colonoscopic diagnosis using on-line learning and differential evolution [PDF]
, 2004 In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line ...
Plagianakos, V.P. +2 more
core +1 more source
Correspondence between neuroevolution and gradient descent
Nature Communications, 2021 We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise ...
Stephen Whitelam +3 more
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Gradient Adversarial Training of Neural Networks
CoRR, 2018 We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other.
Ayan Sinha +3 more
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Applied SciencesNon-convex optimization problems often challenge gradient-based algorithms, such as Gradient Descent. Neural network training, a prominent application of gradient-based methods, heavily relies on their computational efficiency. However, the cost function
Mohammad Sakka, Mohammad Reza Bahrami
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