Notes on Convolutional Neural Networks - Cogprints

Notes on Convolutional Neural NetworksJake BouvrieCenter for Biological and Computational LearningDepartment of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridge, MA 22, 20061 IntroductionThis document discusses the derivation and implementation of Convolutional Neural Networks (CNNs) [3, 4], followed by a few straightforward extensions. Convolutional Neural Networks in-volve many more connections than weights; the architecture itself realizes a form of addition, a Convolutional network automatically provides some degree of translation particular kind of Neural network assumes that we wish to learnfilters, in a data-driven fash-ion, as a means to extract features describing the inputs. The derivation we present is specific totwo-dimensional data and convolutions, but can be extended without much additional effort to anarbitrary number of begin with a description of classical backpropagation in fully connected Networks , followed by aderivation of the backpropagation updates for the filtering and subsampling layers in a 2D convolu-tional Neural

Convolutional neural networks in-volve many more connections than weights; the architecture itself realizes a form of regularization. In addition, a convolutional network automatically provides some degree of translation invariance. This particular kind of neural network assumes that we wish to learn filters, in a data-driven fash-

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  Network, Neural network, Neural, Convolutional, Convolutional networks, Convolutional neural

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