Full-duplex (FD) technique can remarkably boost the network capacity in ...
In recent years, Generative Adversarial Networks (GANs) have produced
si...
Methods such as Layer Normalization (LN) and Batch Normalization (BN) ha...
This paper applies an idea of adaptive momentum for the nonlinear conjug...
In recent years, using orthogonal matrices has been shown to be a promis...
Nonlinear monotone transformations are used extensively in normalizing f...
We consider Convolutional Neural Networks (CNNs) with 2D structured feat...
Batch normalization (BN) is a popular and ubiquitous method in deep lear...
Generative adversarial network (GAN) has become one of the most importan...
Momentum plays a crucial role in stochastic gradient-based optimization
...
With the growing dependence on wind power generation, improving the accu...
Non-orthogonal multiple access (NOMA) is an effective approach to improv...
The paradigm of network function virtualization (NFV) with the support o...
Real-time accurate detection of three-dimensional (3D) objects is a
fund...
Several variants of recurrent neural networks (RNNs) with orthogonal or
...
Direct training based spiking neural networks (SNNs) have been paid a lo...
Space information network (SIN) is an innovative networking architecture...
Convolutional neural network is a very important model of deep learning....
Service-oriented virtual network deployment is based on statistical reso...
In this paper, a comprehensive software-defined networking (SDN) based
t...
In this paper, a dynamic spectrum management framework is proposed to im...
Recurrent neural networks (RNNs) have been successfully used on a wide r...
Enabling high-definition (HD)-map-assisted cooperative driving among
aut...
Massive machine-type communication (mMTC) is a new focus of services in ...
Recurrent Neural Networks (RNNs) are designed to handle sequential data ...