AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 271 to 280 of 780 articles

Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation.

IEEE transactions on neural networks and learning systems Oct 5, 2022
The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We prop...
Humans Neural Networks, Computer Algorithms Learning Supervised Machine Learning Neurons
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Class-Imbalanced Deep Learning via a Class-Balanced Ensemble.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Class imbalance is a prevalent phenomenon in various real-world applications and it presents significant challenges to model learning, including deep learning. In this work, we embed ensemble learning into the deep convolutional neural networks (CNNs...
Neural Networks, Computer Deep Learning
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Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an e...
Neural Networks, Computer Brain Time Factors Magnetic Resonance Imaging Brain Mapping Bayes Theorem Nerve Net Entropy
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Noise Robust Face Hallucination Based on Smooth Correntropy Representation.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Face hallucination technologies have been widely developed during the past decades, among which the sparse manifold learning (SML)-based approaches have become the popular ones and achieved promising performance. However, these SML methods always fai...
Humans Neural Networks, Computer Algorithms Hallucinations
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On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier.

IEEE transactions on neural networks and learning systems Oct 5, 2022
In classification, the use of 0-1 loss is preferable since the minimizer of 0-1 risk leads to the Bayes optimal classifier. However, due to the nonconvexity of 0-1 loss, this optimization problem is NP-hard. Therefore, many convex surrogate loss func...
Neural Networks, Computer Algorithms Sample Size Bayes Theorem
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Graph-Based Bayesian Optimization for Large-Scale Objective-Based Experimental Design.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been d...
Neural Networks, Computer Research Design Computer Simulation Gene Regulatory Networks Bayes Theorem
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Spherical Formation Tracking Control of Nonlinear Second-Order Agents With Adaptive Neural Flow Estimate.

IEEE transactions on neural networks and learning systems Oct 5, 2022
This article addresses the spherical formation tracking control problem of nonlinear second-order vehicles moving in flowfields under both undirected networks and directed, strongly connected networks. Different from the previous adaptive estimate of...
Neural Networks, Computer Algorithms Computer Simulation Feedback Nonlinear Dynamics
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Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Current state-of-the-art visual recognition systems usually rely on the following pipeline: 1) pretraining a neural network on a large-scale data set (e.g., ImageNet) and 2) finetuning the network weights on a smaller, task-specific data set. Such a ...
Humans Neural Networks, Computer Neurons Recognition, Psychology
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Stability and Synchronization of Nonautonomous Reaction-Diffusion Neural Networks With General Time-Varying Delays.

IEEE transactions on neural networks and learning systems Oct 5, 2022
This article investigates the stability and synchronization of nonautonomous reaction-diffusion neural networks with general time-varying delays. Compared with the existing works concerning reaction-diffusion neural networks, the main innovation of t...
Neural Networks, Computer Time Factors Diffusion
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Adaptive Observation-Based Efficient Reinforcement Learning for Uncertain Systems.

IEEE transactions on neural networks and learning systems Oct 5, 2022
This article develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the sy...
Neural Networks, Computer Machine Learning Computer Simulation Learning Reinforcement, Psychology
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