If you are interested in a comparison of neural network architecture and computational performance, see our recent paper. Acknowledgments.
In this article, we'll explore some of the most popular neural network architectures, compare their structures, and discuss their key
The goal of this work is to augment the ROM literature with a comparison between ROM- autoencoder architectures based on fully connected and
Encouraged by the previous promising results, in this work, we compare fully connected neural networks (DNNs), convolutional neural networks (
The different architectures are: feed-forward, Convolutional and, recurrent neural networks, Auto encoder and generational encoders and Deep
I have read a few machine learning paper about proposing new neural network architectures. They then compare their new models to previously
A deep Boltzmann machine with an input layer of k binary units, L hidden layers of n2k binary units, and an output layer of n binary units is a universal approx-imator of stochastic maps, provided L is as in Theorem 7 This is based on the ability of deep Boltzmann machines to represent certain types of transformations that can be rep-resented by feedforward networks (Mont´ ufar, 2015) and on the proof of Theorem 7. A conditional restricted Boltzmann machine with k input binary units, m hidden binary units, and n out-put binary units can approximate a given stochastic map arbitrarily well, whenever it can be represented by a feed-forward network with k input binary units, m hidden linear threshold units, and n output stochastic sigmoid units.
AlexNet has the lowest accuracy and performed the worst when compare to other CNN architectures. Similar findings was also reported by Neha Sharma et al.