[PDF] Top 20 Accelerating the training of convolutional neural network
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Accelerating the training of convolutional neural network
... ways of having large amounts of input data available to the kernel, either the interconnect PCI-e link or the LMem, the latter beeing the logical choice, because of ... See full document
78
PSO optimized Feed Forward Neural Network for offline Signature Classification
... applications Neural networks are widely used where data is non linear and ...result, the networks are tuned by adjusting various network ...optimum network architecture that is topology, layer ... See full document
6
SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation
... standard network architecture with an input layer followed by a convolution layer, a ReLU activation function, a convolution layer also followed by a ReLU, maxpooling and a dropout ...layer. The ... See full document
11
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes
... – The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural ... See full document
5
Rev. bras. ortop. vol.46 número2 en a14v46n2
... images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for ...hybrid neural network based on Kohonen ... See full document
5
APPLYING THE ARTIFICIAL NEURAL NETWORK METHODOLOGY FOR FORECASTING THE TOURISM TIME SERIES
... theory of neural network computation pro- vides interesting techniques that mimic the human brain and nervous system, like we showed ...before. Neural networks are an information ... See full document
6
BATCH GRADIENT METHOD FOR TRAINING OF PI-SIGMA NEURAL NETWORK WITH PENALTY
... convergence of batch gradient method with a penalty condition term for a narration feed forward neural network called pi-sigma neural network, which employ product cells as the ... See full document
10
Implementation of a Neural Network Using Simulator and Petri Nets*
... determining the possible activations in the neural network and achievable ...conditions. The graph of Petri nets can follow all possible input examples of neural ... See full document
6
Egg Hatchability Prediction by Multiple Linear Regression and Artificial Neural Networks
... in the input layer included the entire 496-h incubation period (incubation and hatcher ...by the sensors inside the incubators and the hatchers were randomly divided in two sets – ... See full document
6
Comparison of Statistical and Neural Network Techniques in Predicting Physical Properties of Various Mixtures of Diesel and Biodiesel
... In the present study the biodiesel was prepared in the laboratory and the properties of its blends were experimentally ...analysis of the obtained data was made to ... See full document
4
Neonatal Disease Diagnosis: AI Based Neuro-Genetic Hybrid Approach
... feature of an incremental learning neural network is to find how a network can learn new knownlodge without forgetting the old ...constrain the learning process. This approach ... See full document
8
Research on Spatial Estimation of Soil Property Based on Improved RBF Neural Network
... In the end, the parameters of the number of nodes at the hidden layer, expansion speed and root-mean-square error gained through genetic algorithm optimization are input into RBF ... See full document
8
Assessment of genome-wide prediction by using Bayesian regularized neural networks
... that the simplest Bayesian Regularized Neural Network (BRNN) was able to predict accurately the genomic breeding values from an outbred population based on 3,000 and 1,020 individuals, ... See full document
68
Blood Cell Segmentation Based on Improved Pulse Coupled Neural Network and Fuzzy Entropy
... with the traditional neural network, the pulse coupled neural network does not need the training process to realize the image segmentation, but the ... See full document
12
Metadata of the chapter that will be visualized in SpringerLink
... find the weights of a multilayer perceptron, research on neural networks stagnated until the early eighties with the invention of the Backpropagation ...since the ... See full document
12
A Reminiscence of ”Mastermind”: Iris/Periocular Biometrics by ”In-Set” CNN Iterative Analysis
... Abstract—Convolutional neural networks (CNNs) have emerged as the most popular classification models in biomet- rics ...Under the discriminative paradigm of pattern recognition, CNNs ... See full document
11
Convolutional Neural Network for Face Recognition with Pose and Illumination Variation
... connections. The connection map is decided after implementing 10-fold cross-validation ...number of trainable parameters to learn (trainable parameters impact on memory ...consumption). The purpose ... See full document
14
Performance of ANN using Back Propagation Algorithm for Medical Diagnosis System
... Artificial neural networks [1] provide a powerful tool to help doctors to analyze, model and make sense of complex clinical data across a broad range of medical ...applications of artificial ... See full document
5
SOFTWARE EFFORT ESTIMATION FRAMEWORK TO IMPROVE ORGANIZATION PRODUCTIVITY USING EMOTION RECOGNITION OF SOFTWARE ENGINEERS IN SPONTANEOUS SPEECH
... part of any organisation in general and software industry in p ...from the employee’s of the ...working. Of course, in other industries this may be achieved without man ...line ... See full document
7
Topic: Modelling and Forecasting A new approach to modelling and forecasting monthly overnights in the Northern Region of Portugal
... is the most popular neural network training algorithm that has been used to perform learning on feedforward neural ...each of the processing units in the ... See full document
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