Results 21 to 30 of about 852,410 (222)
Sparsity in Reservoir Computing Neural Networks [PDF]
2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2020 Reservoir Computing (RC) is a well-known strategy for designing Recurrent Neural Networks featured by striking efficiency of training. The crucial aspect of RC is to properly instantiate the hidden recurrent layer that serves as dynamical memory to the system.
openaire +3 more sources
Известия Томского политехнического университета: Инжиниринг георесурсов, 2019 The relevance of the research is caused by the necessity to develop modern computer vision systems for monitoring hazardous technological objects of oil and gas industry.
Ivan V. Zoev +2 more
doaj +1 more source
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
openaire +4 more sources
Physics Inspired Deep Neural Networks for Top Quark Reconstruction [PDF]
EPJ Web of Conferences, 2020 Deep neural networks (DNNs) have been applied to the fields of computer vision and natural language processing with great success in recent years. The success of these applications has hinged on the development of specialized DNN architectures that take ...
Greif Kevin, Lannon Kevin
doaj +1 more source
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.
F. Araújo, B. Ribeiro, L. Rodrigues
openaire +4 more sources
Acta Dermato-Venereologica, 2022 Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with 6 ...
Martin Gillstedt +8 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Analog computation via neural networks [PDF]
[1993] The 2nd Israel Symposium on Theory and Computing Systems, 1994 AbstractWe pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research.Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of “neurons”. If allowed exponential time for computation, they turn out to have unbounded power.
Hava T. Siegelmann, Eduardo D. Sontag
openaire +1 more source
Computational and topological properties of neural networks by means of graph-theoretic parameters
Alexandria Engineering Journal, 2023 A neural network is a computer system modeled on the nerve tissue and nervous system. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.
Asad Khan +5 more
doaj