IEEE transactions on neural networks and learning systems
Nov 30, 2022
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 limited to computer vision and natural language processing, it attracted ...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed training, where a model is replicated into several computational nodes th...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
In this brief, we consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. The idea of neural codes, based on fully connected layers' activations, is extended by incorporating the info...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Personalized news recommendation aims to recommend news articles to customers, by exploiting the personal preferences and short-term reading interest of users. A practical challenge in personalized news recommendations is the lack of logged user inte...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Co...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
This article studies the problem of the stability for Markovian neural networks (MNNs) with time delay. The transition rate is considered to be generally hybrid, which treats those existing ones as its special cases. The introduced generally hybrid t...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
This article concerns with terminal recurrent neural network (RNN) models for time-variant computing, featuring finite-valued activation functions (AFs), and finite-time convergence of error variables. Terminal RNNs stand for specific models that adm...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
As a unified framework for graph neural networks, message passing-based neural network (MPNN) has attracted a lot of research interest and has been shown successfully in a number of domains in recent years. However, because of over-smoothing and vani...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Popular network pruning algorithms reduce redundant information by optimizing hand-crafted models, and may cause suboptimal performance and long time in selecting filters. We innovatively introduce adaptive exemplar filters to simplify the algorithm ...
Sensors (Basel, Switzerland)
Nov 30, 2022
In this study, we tested the ability of a machine-learning model (ML) to evaluate different user interface designs within the defined boundaries of some given software. Our approach used ML to automatically evaluate existing and new web application d...