neural network training
Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training
... There are a lot of research to improve Levenberg-Marquardt algorithm to train neural networks, in last years, for example, (An Ru et. al., 2016) in [1] suggested improving the LM algorithm by direct calculation of ...
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Artificial Neural Network Training By Using Regrouping Particle Swarm Optimization
... Artificial Neural Network training by using Re-grouping Particle Swarm Optimization ...ANN training by using Regrouping-PSO the objective function is taken as the Mean Square Error (MSE) ...
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Measuring the Effects of Data Parallelism on Neural Network Training
... for neural network ...the training steps needed as a function of batch size and confirming the boundaries of the three basic scaling regimes requires expensive ...
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Comparison of Neural Network Training Algorithms for Classification of Heart Diseases
... Artificial neural network (ANN) technique can be used to predict or classification patients getting a heart ...different training algorithms for ANN. We compared eight neural network ...
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Parallel computing for artificial neural network training
... three tests in terms of performance gain. As expected, a lesser amount of elapsed time has been recorded from a single machine tests. But the performance gain tends to stop at a certain size of a number of nodes used in ...
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SecureNN: Efficient and Private Neural Network Training
... secure training was that of Lindell and Pinkas [29] who provided algorithms to execute decision tree based training over shared ...in neural network training when data is horizontally ...
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Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell
... artificial neural network trained at every time, separately, by one algorithm among the optimization algorithms of gradient descent (Levenberg-Marquardt, Gauss-Newton, Quasi-Newton, steepest descent and ...
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Comparison of Different Neural Network Training Algorithms with Application to Face Recognition
... computation of the average face and subtraction of the average face from the training faces. First, we need to calculate the average image. Then, that image has to be subtracted from each original image. Next step ...
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Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction
... the training phase, the Recurrent Neural Network is presented with a set of the training data from the dataset and the input weights are adjusted by using the ABC algorithm as a learning ...
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The use of adversaries for optimal neural network training
... the training algorithm parameters (batch size, learning rate, training steps ...TensorFlow network was found using HyperBand [9], which narrows down the best configuration from a large random sample ...
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Configuring spiking neural network training algorithms
... In 1985, Dr. Marian Diamond published anatomical studies of slivers of Einstein’s brain where she claimed that Einstein’s brain had a greater ratio of glial cells to neu- rons compared to a sample group of 11 other ...
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Swarm-based Algorithms for Neural Network Training
... the training loss results. Much like the training loss, PSO generated the best networks for testing loss based on the rank and the number of times that PSO generated the network that was ranked first ...
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Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
... convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of ...two network ...
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Training Neural Network Elements Created From Long Shot Term Memory
... provide training complex recurrent neural ...that training recurrent networks is more complex than the type of training feed forward neural ...and training process achieves a ...
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Improved PSO Algorithm for Training of Neural Network in Co design Architecture
... The NN is invented to represent a human brain. In order to be used in the testing phase, the NN need to be trained in the training phase [3–5]. The back-propagation (BP) algorithm has investigated in previous ...
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Artificial neural networks training acceleration through network science strategies
... The revised fraction goodness is independent from the application domain; thus, a relatively low zeta is always a good practice. Of course, according to the specific scenario considered, the performance may be higher ...
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Training a Quantum Neural Network to Solve the Contextual Multi Armed Bandit Problem
... quantum neural networks has been a long ...the network while maintaining the unitary property of the ...Quantum neural networks of- fer a quantum advantage, where in some problems, a classical ...
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Supervised Machine Learning for Training a Neural Network as 5:2 Compressor
... ANN, training has been accomplished in Keras library for 1000 epochs (is a round when the whole training set passed through ANN) which gives accuracy value for each of the approximate 5:2 ...
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Skin Tone Based Face Recognition and Training using Neural Network
... train Network to achieve a balance between the network’s ability to respond and the ability to give a reasonable response to the input that is similar, but not identical to the one used in the ...The ...
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Complex Valued Recurrent Neural Network: From Architecture to Training
... dynamical systems. There are two principle ways of modeling dynamical systems: 1) use a feed-forward neu- ral network and use delayed inputs or 2) use a recurrent architecture and model the dynamics itself. The ...
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