IEEE transactions on neural networks and learning systems
Jul 6, 2023
This article focuses on the finite-time and fixed-time synchronization of a class of coupled discontinuous neural networks, which can be viewed as a combination of the Hindmarsh-Rose model and the Kuramoto model. To this end, under the framework of F...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Direct-optimization-based dictionary learning has attracted increasing attention for improving computational efficiency. However, the existing direct optimization scheme can only be applied to limited dictionary learning problems, and it remains an o...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both structure and attribute features have achieved in many real-world applica...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Though deep learning-based saliency detection methods have achieved gratifying performance recently, the predicted saliency maps still suffer from the boundary challenge. From the perspective of foreground-background separation, this article attempts...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Training deep neural networks (DNNs) rested heavily on efficient local solvers. Due to their local property, local solvers are sensitive to initialization and hyperparameters. In this article, a systematical method for finding multiple high-quality l...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Word-character lattice models have been proved to be effective for some Chinese natural language processing (NLP) tasks, in which word boundary information is fused into character sequences. However, due to the inherently unidirectional sequential na...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Optimal tracking in switched systems with fixed mode sequence and free final time is studied in this article. In the optimal control problem formulation, the switching times and the final time are treated as parameters. For solving the optimal contro...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
The Cox proportional hazard model has been widely applied to cancer prognosis prediction. Nowadays, multi-modal data, such as histopathological images and gene data, have advanced this field by providing histologic phenotype and genotype information....
IEEE transactions on neural networks and learning systems
Jul 6, 2023
The state-of-the-art reinforcement learning (RL) techniques have made innumerable advancements in robot control, especially in combination with deep neural networks (DNNs), known as deep reinforcement learning (DRL). In this article, instead of revie...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Deep learning models have been able to generate rain-free images effectively, but the extension of these methods to complex rain conditions where rain streaks show various blurring degrees, shapes, and densities has remained an open problem. Among th...