Abstract: Convolutional neural networks (CNNs) are a specialized type of deep neural networks (DNNs) that utilize a mathematical operation called convolution instead of general matrix multiplication ...

This repository contains a number of convolutional neural network visualization techniques implemented in PyTorch. Note: I removed cv2 dependencies and moved the repository towards PIL. A few things ...

Abstract: This chapter presents convolutional neural networks (CNNs) that are often classifiers, so a CNN can be classifying neural network. A CNN is an ANN that includes at least one convolutional ...

1st version of the High-order Graph Convolutional Recurrent Neural Network Structure The 1st version of Traffic Graph Convolutional LSTM. The code of this model is in the Code_V1 folder. Environment: ...

Fully connected neural networks in the infinite-width limit often outperform finite-width models, while convolutional networks excel at finite widths. Here, the authors uncover how convolutional ...

DO trends have complex nonlinear characteristics. Therefore, the accurate prediction of DO is challenging. On this basis, a two-dimensional data-driven convolutional neural network model (2DD-CNN) is ...