Results 41 to 50 of about 1,645,295 (287)
Reconfigurable Computation in Spiking Neural Networks [PDF]
IEEE Access, 2020 The computation of rank ordering plays a fundamental role in cognitive tasks and offers a basic building block for computing arbitrary digital functions. Spiking neural networks have been demonstrated to be capable of identifying the largest k out of N analog input signals through their collective nonlinear dynamics.
Fabio Schittler Neves, Marc Timme
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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.
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Analog computation via neural networks [PDF]
[1993] The 2nd Israel Symposium on Theory and Computing Systems, 1994 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hava T. Siegelmann, Eduardo D. Sontag
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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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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
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A survey of the recent architectures of deep convolutional neural networks [PDF]
Artificial Intelligence Review, 2019 Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Asifullah Khan +3 more
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Computer networks anomaly detection by using PCA & pattern recognition [PDF]
Mathematics and Computational SciencesThe detection of anomalies in computer networks is one of the most considerable challenges that experts in this field are facing nowadays. Thus far, different artificial intelligence methods and algorithms have been proposed, tested, and utilized for ...
Elham Bideh, Javad Vahidi
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Efficient Processing of Deep Neural Networks: A Tutorial and Survey [PDF]
Proceedings of the IEEE, 2017 Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics.
V. Sze +3 more
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SA-Net: Shuffle Attention for Deep Convolutional Neural Networks [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021 Attention mechanisms, which enable a neural network to accurately focus on all the relevant elements of the input, have become an essential component to improve the performance of deep neural networks.
Qing-Long Zhang, Yubin Yang
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Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks
Applied Sciences, 2021 The great commitment in different areas of computer science for the study of computer networks used to fulfill specific and major business tasks has generated a need for their maintenance and optimal operability. Distributed denial of service (DDoS) is a
Andrés Chartuni, José Márquez
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