This document discusses the derivation and implementation of convolutional neural
networks (CNNs)[3, 4], followed by a few straightforward extensions. Convolutional neural …

Neural networks are a subset of the field of artificial intelligence (AI). The predominant types
of neural networks used for multidimensional signal processing are deep convolutional …

A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

Convolutional Networks Page 1 Convolutional Networks Lecture slides for Chapter 9 of Deep
Learning Ian Goodfellow 2016-09-12 Adapted by mn for CMPS 392 Page 2 (Goodfellow 2016) …

Recent success in training deep neural networks have prompted active investigation into the
features learned on their intermediate layers. Such research is difficult because it requires …

A key challenge in designing convolutional network models is sizing them appropriately.
Many factors are involved in these decisions, including number of layers, feature maps …

Many deep neural networks trained on natural images exhibit a curious phenomenon in
common: on the first layer they learn features similar to Gabor filters and color blobs. Such …

We introduce a probability distribution, combined with an efficient sampling algorithm, for
weights and biases of fully-connected neural networks. In a supervised learning context, no …

This is a note that describes how a Convolutional Neural Network (CNN) operates from a
mathematical perspective. This note is self-contained, and the focus is to make it …

Establishing a solid theoretical foundation for structured deep neural networks is greatly
desired due to the successful applications of deep learning in various practical domains …