A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

certain existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs. A convolutional neural network layer, in the...

networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance...

artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are...

recurrent neural networks and convolutional neural networks, renewed interest in ANNs. The 2010s saw the development of a deep neural network (i.e., one...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia...

AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance in...

LeNet is a series of convolutional neural network architectures created by a research group in AT&T Bell Laboratories during the 1988 to 1998 period, centered...

The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family...

Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights...

closely mimic biological neural organization. The idea is to add structures called "capsules" to a convolutional neural network (CNN), and to reuse output...

is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015, YOLO has...

Alex Krizhevsky and Geoffrey Hinton, he co-invented AlexNet, a convolutional neural network. Sutskever co-founded and was a former chief scientist at OpenAI...

Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and...

convolutional neural network best performance was 0.25 percent error rate. As of August 2018, the best performance of a single convolutional neural network...

and 2) model context at each layer of the network. It is essentially a 1-d convolutional neural network (CNN). Shift-invariant classification means...

An optical neural network is a physical implementation of an artificial neural network with optical components. Early optical neural networks used a photorefractive...

U-Net is a convolutional neural network that was developed for image segmentation. The network is based on a fully convolutional neural network whose architecture...

A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions...

MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision...

called "positive part") was critical for object recognition in convolutional neural networks (CNNs), specifically because it allows average pooling without...

In the VC-6 standard an up-sampler developed with an in-loop Convolutional Neural Network is provided to optimize the detail in the reconstructed image...

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine...

physics-informed neural networks. Differently from traditional machine learning algorithms, such as feed-forward neural networks, convolutional neural networks, or...

A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on...

Generators (QRNGs) to machine learning models including Neural Networks and Convolutional Neural Networks for random initial weight distribution and Random...

Ahn, Byeongyong; Ik Cho, Nam (3 April 2017). "Block-Matching Convolutional Neural Network for Image Denoising". arXiv:1704.00524 [Vision and Pattern Recognition...

Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network". Proceedings of the 2018 Conference of the North American Chapter...

Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed...