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Neural networks : the official journal of the International Neural Network Society
Dec 4, 2024
To efficiently solve the time-varying convex quadratic programming (TVCQP) problem under equational constraint, an adaptive variable-parameter dynamic learning network (AVDLN) is proposed and analyzed. Being different from existing varying-parameter ...
Neural networks : the official journal of the International Neural Network Society
Dec 4, 2024
EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals t...
Neural networks : the official journal of the International Neural Network Society
Dec 4, 2024
This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering...
Neural networks : the official journal of the International Neural Network Society
Dec 4, 2024
Human pose estimation is one of the most critical and challenging problems in computer vision. It is applied in many computer vision fields and has important research significance. However, it is still a difficult challenge to strike a balance betwee...
Neural networks : the official journal of the International Neural Network Society
Dec 4, 2024
The increasing utilization of deep neural networks (DNNs) in safety-critical systems has raised concerns about their potential to exhibit undesirable behaviors. Consequently, DNN repair/patching arises in response to the times, and it aims to elimina...
Neural networks : the official journal of the International Neural Network Society
Dec 4, 2024
Stability analysis is an essential aspect of studying the generalization ability of deep learning, as it involves deriving generalization bounds for stochastic gradient descent-based training algorithms. Adversarial training is the most widely used d...
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt ...
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, ti...
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
Multivariate time series classification is of great importance in practical applications and is a challenging task. However, deep neural network models such as Transformers exhibit high accuracy in multivariate time series classification but lack int...
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
Biomedical signals, encapsulating vital physiological information, are pivotal in elucidating human traits and conditions, serving as a cornerstone for advancing human-machine interfaces. Nonetheless, the fidelity of biomedical signal interpretation ...