Results 41 to 50 of about 5,989,108 (296)
Learning Trajectories of Hamiltonian Systems with Neural Networks [PDF]
, 2022 Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton\u27s equations of ...
Haitsiukevich, Katsiaryna +1 more
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Evolutionary Neural Logic Networks in Two Medical Decision Tasks [PDF]
, 2004 Two real-world problems of the medical domain are addressed in this work using a novel approach belonging to the area of neural-symbolic systems. Specifically,we apply evolutionary techniques for the development of neural logic networks of arbitrary ...
Dounias, Georgios, Tsakonas, Athanasios
core +8 more sources
Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives
Energies, 2023 In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science.
Dorian Skrobek +8 more
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Computing with dynamic attractors in neural networks [PDF]
Biosystems, 1995 In this paper we report on some new architectures for neural computation, motivated in part by biological considerations. One of our goals is to demonstrate that it is just as easy for a neural net to compute with arbitrary attractors--oscillatory or chaotic--as with the more usual asymptotically stable fixed points.
Hirsch, MW, Baird, B
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A neural network for shortest path computation [PDF]
IEEE Transactions on Neural Networks, 2001 This paper presents a new neural network to solve the shortest path problem for inter-network routing. The proposed solution extends the traditional single-layer recurrent Hopfield architecture introducing a two-layer architecture that automatically guarantees an entire set of constraints held by any valid solution to the shortest path problem.
Filipe Araújo +2 more
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Small Neural Networks can Denoise Image Textures Well: a Useful Complement to BM3D
Image Processing On Line, 2016 Recent years have seen a surge of interest in deep neural networks fueled by their successful applications in numerous image processing and computer vision tasks. However, such applications typically come with huge computational loads.
Yi-Qing Wang
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Automatic Pipeline Parallel Training Framework for General-purpose Computing Devices [PDF]
Jisuanji kexueTraining large-scale neural networks usually exceeds the memory and computing capacity of a single computing node,which requires distributed training using multiple nodes.Existing distributed deep learning frameworks are mainly designed for specific ...
ZHONG Zhenyu, LIN Yongliang, WANG Haotian, LI Dongwen, SUN Yufei, ZHANG Yuzhi
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Performance Analysis of Deep Neural Networks Using Computer Vision [PDF]
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2021 INTRODUCTION: In recent years, deep learning techniques have been made to outperform the earlier state-of-the-art machine learning techniques in many areas, with one of the most notable cases being computer vision.
Nidhi Sindhwani +5 more
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Benchmarking Neural Networks For Quantum Computations [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Revised substantially, and resubmitted to IEEE Transactions on Neural Networks and Learning ...
Nam H. Nguyen +3 more
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Harnessing Advanced Neural Architectures: A Comprehensive Approach to Stock Market Prediction Using ANN, BPNN, and GAN [PDF]
SHS Web of ConferencesThe advent of advanced neural network models has revolutionized the field of machine learning, enabling breakthroughs in various domains such as computer vision, natural language processing, and predictive analytics.
Wang Yang
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