Results 31 to 40 of about 25,760 (169)
CoRR, 2019 We show how a declarative functional programming specification of backpropagation yields a visual and transparent implementation within spreadsheets. We call our method Visual Backpropagation. This backpropagation implementation exploits array worksheet formulas, manual calculation, and has a sequential order of computation similar to the processing of
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Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
Entropy, 2020 Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural ...
Adrian Moldovan +2 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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BiomimeticsThe human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s ...
Luis Irastorza-Valera +3 more
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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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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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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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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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Towards New Generation, Biologically Plausible Deep Neural Network Learning
Sci, 2022 Artificial neural networks in their various different forms convincingly dominate machine learning of the present day. Nevertheless, the manner in which these networks are trained, in particular by using end-to-end backpropagation, presents a major ...
Anirudh Apparaju, Ognjen Arandjelović
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