Results 11 to 20 of about 219,698 (314)
AI-Track-tive: open-source software for automated recognition and counting of surface semi-tracks using computer vision (artificial intelligence) [PDF]
Geochronology, 2021 A new method for automatic counting of etched fission tracks in minerals is described and presented in this article. Artificial intelligence techniques such as deep neural networks and computer vision were trained to detect fission surface semi-tracks on
S. Nachtergaele, J. De Grave
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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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An Interactive Visualization for Feature Localization in Deep Neural Networks
Frontiers in Artificial Intelligence, 2020 Deep artificial neural networks have become the go-to method for many machine learning tasks. In the field of computer vision, deep convolutional neural networks achieve state-of-the-art performance for tasks such as classification, object detection, or ...
Martin Zurowietz, Tim W. Nattkemper
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Review of dynamic gesture recognition
Virtual Reality & Intelligent Hardware, 2021 In recent years, gesture recognition has been widely used in the fields of intelligent driving, virtual reality, and human-computer interaction. With the development of artificial intelligence, deep learning has achieved remarkable success in computer ...
Yuanyuan SHI +4 more
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Transition between individually different and common features in skilled drumming movements
Frontiers in Sports and Active Living, 2022 Why do professional athletes and musicians exhibit individually different motion patterns? For example, baseball pitchers generate various pitching forms, e.g., variable wind-up, cocking, and follow-through forms.
Ken Takiyama +2 more
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DETECTION OF ATTACKS ON A COMPUTER NETWORK BASED ON THE USE OF NEURAL NETWORKS COMPLEX
Nauka ta progres transportu, 2020 Purpose. The article is aimed at the development of a methodology for detecting attacks on a computer network. To achieve this goal the following tasks were solved: to develop a methodology for detecting attacks on a computer network based on an ensemble
I. V. Zhukovyts'kyy +3 more
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Neural computing with coherent laser networks
Nanophotonics, 2023 Abstract We show that coherent laser networks (CLNs) exhibit emergent neural computing capabilities. The proposed scheme is built on harnessing the collective behavior of laser networks for storing a number of phase patterns as stable fixed points of the governing dynamical equations and retrieving such patterns through proper ...
Mohammad‐Ali Miri, Vinod Menon
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Quantum Neural Network for Quantum Neural Computing
Research, 2023 Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real ...
Min-Gang Zhou +5 more
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Lazy training of radial basis neural networks [PDF]
, 2006 Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space.
Galván, Inés M. +5 more
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Graph Neural Networks in Computer Vision - Architectures, Datasets and Common Approaches [PDF]
, 2023 Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph ...
Lukasikt, S, Krzywda, M, Gandomi, AH
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