Regularization Of Neural Networks Using Dropconnect. En apprentissage automatique un réseau de neurones convolutifs ou réseau de neurones à convolution (en anglais CNN ou ConvNet pour Convolutional Neural Networks) est un type de réseau de neurones artificiels acycliques (feedforward) dans lequel le motif de connexion entre les neurones est inspiré par le cortex visuel des animaux Les neurones de cette région du.
Regularization of neural networks using dropconnect L Wan M Zeiler S Zhang Y LeCun R Fergus 30th International Conference on Machine Learning (ICML 2013) 10581066 2013.
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Deep convolutional neural networks assisted by architectural design strategies make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object.
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The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems The database is also widely used for training and testing in the field of machine learning It was created by “remixing” the samples from NIST’s original datasets.
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PDF fileIt has long been known that an ensemble of multiple neural networks generally yields better predictions than a single network in the ensemble This effect has also been indirectly exploited when training a single network through dropout (Srivastava et al 2014) dropconnect (Wan et al 2013) or stochastic depth (Huang et al 2016) regularization methods and in swapout.
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Convolutional neural networks are more complex than standard multilayer perceptrons so we will start by using a simple structure to begin with that uses all of the elements for state of the art results Below summarizes the network architecture The first hidden layer is a convolutional layer called a Convolution2D The layer has 32 feature maps which with.