Results 41 to 50 of about 25,760 (169)
Robust Implicit Backpropagation
CoRR, 2018 Arguably the biggest challenge in applying neural networks is tuning the hyperparameters, in particular the learning rate. The sensitivity to the learning rate is due to the reliance on backpropagation to train the network. In this paper we present the first application of Implicit Stochastic Gradient Descent (ISGD) to train neural networks, a method ...
Francois Fagan, Garud Iyengar
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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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Recunoașterea unei cifre scrise de mână folosind o rețea neuronală convoluțională și biblioteca TensorFlow unei cifre scrise de mână folosind o rețea neuronală convoluțională și biblioteca TensorFlow [PDF]
Revista Română de Informatică și Automatică, 2019 In this paper it is proposed to solve a visual problem of recognizing a handwritten figure. A machine learning technique will be used in which a result is produced based on previous experience.
Paul TEODORESCU
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Frontiers in Computational Neuroscience, 2022 Backpropagation (BP) has been used to train neural networks for many years, allowing them to solve a wide variety of tasks like image classification, speech recognition, and reinforcement learning tasks.
Yoshimasa Kubo +2 more
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Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World
JOIN: Jurnal Online Informatika, 2019 The Internet today has become a primary need for its users. According to market research company e-Marketer, there are 25 countries with the largest internet users in the world.
Sunil Setti, Anjar Wanto
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An In-Depth Study of Stochastic Backpropagation
Advances in Neural Information Processing Systems 35, 2022 NeurIPS ...
Jun Fang +5 more
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Backpropagation-Friendly Eigendecomposition
CoRR, 2019 Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it with the Power Iteration method, particularly when dealing with large matrices.
Wang, Wei +4 more
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Revista Tecnológica, 2014 La aplicación de modelos matemáticos en el manejo de cuencas hidrográficas tiene requerimientos exigentes de información y en su mayoría no han sido desarrollados para ser aplicados en regiones de montaña.
Jaime Eduardo Veintimilla +1 more
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Nonlinear backpropagation: doing backpropagation without derivatives of the activation function [PDF]
IEEE Transactions on Neural Networks, 1997 The conventional linear backpropagation algorithm is replaced by a nonlinear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in hardware realizations of neural processors. In this paper we derive the nonlinear backpropagation algorithms in the framework of recurrent backpropagation ...
John A. Hertz +3 more
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A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction
Data, 2018 Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits.
Alexander Vlasenko +3 more
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