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Backpropagation Neural Tree [PDF]
Neural Networks, 2022 We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biological
Varun Ojha 0001, Giuseppe Nicosia
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Backpropagation and the brain [PDF]
Nature Reviews Neuroscience, 2020 During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in deep artificial neural networks, but historically it
Timothy P. Lillicrap +4 more
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Memorized sparse backpropagation [PDF]
Neurocomputing, 2020 Accepted to ...
Zhiyuan Zhang 0001 +4 more
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CoRR, 2022 Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts.
Johannes Pöppelbaum, Andreas Schwung
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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022 Garbage is a problem that needs an in-depth study in urban areas because the development of an area has consequences on increasing population density, facilities and infrastructure, public services, and other aspects that impact increasing the volume of ...
Luqman Hakim +4 more
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Gradients without Backpropagation
CoRR, 2022 Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that also includes the forward mode.
Baydin, AG +4 more
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INFLATION FORECASTS IN AMBON USING NEURAL NETWORK APPLICATIONS BACKPROPAGATION
Barekeng, 2022 An artificial Neural Network is the processing of information systems on certain characteristics which are artificial representations based on human neural networks.
Mozart W Talakua +3 more
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APPLICATION OF THE BACKPROPAGATION METHOD TO PREDICT RAINFALL IN NORTH SUMATRA PROVINCE
Barekeng, 2023 Natural disasters are to blame for the high level of community loss. This is due to the community's lack of information about potential disasters around them. As a result, public understanding of disaster response is extremely low.
Rinjani Cyra Nabila +3 more
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Image Analysis of Diabetic Retinopathy Disease Based on Artificial Neural Network Algorithms
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2020 Diabetic retinopathy is a complication of diabetes in the form of damage to the retina of the eye. High levels of glucose in the blood are the cause of small capillary blood vessels to rupture and can cause blindness.
Tri Astuti, Gesha Agus Setiawan
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Real-time stability assessment in smart cyber-physical grids: a deep learning approach
IET Smart Grid, 2020 The increasing coupling between the physical and communication layers in the cyber-physical system (CPS) brings up new challenges in system monitoring and control.
Farzad Darbandi +6 more
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