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...

with ℓ being the ℓ-th layer of this residual neural network. While the forward propagation of a residual neural network is done by applying a sequence of...

techniques were developed to train such networks: the Highway Network (published in May), and the residual neural network, or ResNet (December). ResNet behaves...

Scientist at Google DeepMind. He is known as one of the creators of residual neural network (ResNet). He attended the public Guangzhou Zhixin High School in...

machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular...

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...

Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that...

(2017). "Enhanced Deep Residual Networks for Single Image Super-Resolution". arXiv:1707.02921 [cs.CV]. "Generative Adversarial Network and Super Resolution...

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural circuitry...

feed-forward neural networks with ReLU as activation functions. Similar results that can be directly applied to residual neural networks were also obtained...

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

In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up...

machine learning, a neural field (also known as implicit neural representation, neural implicit, or coordinate-based neural network), is a mathematical...

extensive mass of rock or land surface Inselberg Mesa Monadnock Residual neural network Residual volume, the amount of air left in the lungs after a maximal...

highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. In Dec 2015, the residual neural network (ResNet)...

evaluation. These neural networks are designed to run on GPU, unlike traditional engines. It originally used residual neural networks, but in 2022 switched...

ResNet most prominently refers to residual neural network, a type of artificial neural network characterized by introducing additional connections that...

decoder layers have a feed-forward neural network for additional processing of their outputs and contain residual connections and layer normalization...

parameter initialization describes the initial step in creating a neural network. A neural network contains trainable parameters that are modified during training:...

represents a scene as a radiance field parametrized by a deep neural network (DNN). The network predicts a volume density and view-dependent emitted radiance...

later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to their...

developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's...

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...

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...

Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K. I.; and Culotta, Aron (eds.), Advances in Neural Information...

trained 6 experts, each being a "time-delayed neural network" (essentially a multilayered convolution network over the mel spectrogram). They found that...

machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It is...

Nikos (2016-05-23). "Wide Residual Networks". arXiv:1605.07146 [cs.CV]. Zoph, Barret; Le, Quoc V. (2016-11-04). "Neural Architecture Search with Reinforcement...

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