AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 211 to 220 of 780 articles

Delay-Dependent Switching Approaches for Stability Analysis of Two Additive Time-Varying Delay Neural Networks.

IEEE transactions on neural networks and learning systems Nov 30, 2022
This article analyzes the exponentially stable problem of neural networks (NNs) with two additive time-varying delay components. Disparate from the previous solutions on this similar model, switching ideas, that divide the time-varying delay interval...
Neural Networks, Computer Algorithms Computer Simulation Time Factors
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IBLF-Based Adaptive Neural Control of State-Constrained Uncertain Stochastic Nonlinear Systems.

IEEE transactions on neural networks and learning systems Nov 30, 2022
In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to sym...
Neural Networks, Computer Computer Simulation Learning Nonlinear Dynamics Uncertainty
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On Information Plane Analyses of Neural Network Classifiers-A Review.

IEEE transactions on neural networks and learning systems Nov 30, 2022
We review the current literature concerned with information plane (IP) analyses of neural network (NN) classifiers. While the underlying information bottleneck theory and the claim that information-theoretic compression is causally linked to generali...
Neural Networks, Computer Data Compression Information Theory
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Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation.

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...
Neural Networks, Computer Neurons Entropy Information Theory
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Supervised Learning in Neural Networks: Feedback-Network-Free Implementation and Biological Plausibility.

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...
Neural Networks, Computer Algorithms Feedback Supervised Machine Learning Neurons
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Data-Independent Structured Pruning of Neural Networks via Coresets.

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...
Neural Networks, Computer Algorithms Neurons Data Compression
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End-to-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery.

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...
Neural Networks, Computer Learning Deep Learning Probability Reinforcement, Psychology
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Proximal Online Gradient Is Optimum for Dynamic Regret: A General Lower Bound.

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...
Neural Networks, Computer Algorithms Emotions
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Centralized and Collective Neurodynamic Optimization Approaches for Sparse Signal Reconstruction via L₁-Minimization.

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...
Neural Networks, Computer Feedback
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Toward Region-Aware Attention Learning for Scene Graph Generation.

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...
Humans Neural Networks, Computer Learning Animals Male Attention Horses
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