Results 41 to 50 of about 129,225 (287)
Química Nova, 2001 Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special
Eduardo O. de Cerqueira +3 more
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Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks
Sensors, 2021 The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images.
Raghu G. Raj +2 more
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Cellular and Network Mechanisms for Temporal Signal Propagation in a Cortical Network Model
Frontiers in Computational Neuroscience, 2019 The mechanisms underlying an effective propagation of high intensity information over a background of irregular firing and response latency in cognitive processes remain unclear. Here we propose a SSCCPI circuit to address this issue. We hypothesize that
Zonglu He
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Research on three-step accelerated gradient algorithm in deep learning
Statistical Theory and Related Fields, 2022 Gradient descent (GD) algorithm is the widely used optimisation method in training machine learning and deep learning models. In this paper, based on GD, Polyak's momentum (PM), and Nesterov accelerated gradient (NAG), we give the convergence of the ...
Yongqiang Lian +2 more
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Recurrent backpropagation and the dynamical approach to adaptive neural computation [PDF]
, 1989 Error backpropagation in feedforward neural network models is a popular learning algorithm that has its roots in nonlinear estimation and optimization.
Pineda, Fernando J.
core
Additive Manufacturing of NiTi Shape Memory Alloys for Elastocaloric Applications: A Review
Advanced Functional Materials, EarlyView.Additive manufacturing enables complex NiTi architectures that overcome key limitations in elastocaloric refrigeration, including poor heat transfer and high mechanical work input. This review surveys recent advances in LPBF‐ and DED‐fabricated NiTi shape memory alloys for elastocaloric applications, highlighting process–structure–performance ...
Ignatius Andre Setiawan +7 more
wiley +1 more source
Backward Signal Propagation: A Symmetry-Based Training Method for Neural Networks
AlgorithmsWhile backpropagation (BP) has long served as the cornerstone of training deep neural networks, it relies heavily on strict differentiation logic and global gradient information, lacking biological plausibility. In this paper, we systematically present a
Kun Jiang, Zhihong Fu
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Integration Method with Backpropagation [PDF]
Al-Rafidain Journal of Computer Sciences and Mathematics, 2005 In this research, a new method is discovered (combined method) to accelerate the backpropagation network by using the expected values of source units for updating weights, we mean the expected value of unit by the sum of the output of the unit and its ...
Nidhal AL-Assady +2 more
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Optimization without Backpropagation
CoRR, 2022 11 pages, 6 figures, associated implementation available at https://github.com/gbelouze/forward ...
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Stochastic Digital Backpropagation [PDF]
IEEE Transactions on Communications, 2014 In this paper, we propose a novel detector for single-channel long-haul coherent optical communications, termed stochastic digital backpropagation (SDBP), which takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments.
Naga VishnuKanth Irukulapati +3 more
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