IEEE transactions on neural networks and learning systems
Nov 30, 2022
In this work, we investigate the use of three information-theoretic quantities-entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler (KL) divergence-to understand and study the behavior of alre...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
The well-known backpropagation learning algorithm is probably the most popular learning algorithm in artificial neural networks. It has been widely used in various applications of deep learning. The backpropagation algorithm requires a separate feedb...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Model compression is crucial for the deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majorit...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hiera...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
In online learning, the dynamic regret metric chooses the reference oracle that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is par...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the L -minimization problem. First, two centralized neurodynamic approaches are designed based on the augmented Lagrange meth...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Scene graph generation (SGGen) is a challenging task due to a complex visual context of an image. Intuitively, the human visual system can volitionally focus on attended regions by salient stimuli associated with visual cues. For example, to infer th...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
One of the significant tasks in remaining useful life (RUL) prediction is to find a good health indicator (HI) that can effectively represent the degradation process of a system. However, it is difficult for traditional data-driven methods to constru...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with...
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Neural architecture search (NAS) is gaining more and more attention in recent years because of its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, the searching process usua...