AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 541 to 550 of 814 articles

Adversarial Examples: Opportunities and Challenges.

IEEE transactions on neural networks and learning systems Sep 17, 2019
Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles, and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which ar...
Deep Learning Pattern Recognition, Automated Humans Artificial Intelligence Neural Networks, Computer
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Unsupervised Anomaly Detection With LSTM Neural Networks.

IEEE transactions on neural networks and learning systems Sep 13, 2019
We investigate anomaly detection in an unsupervised framework and introduce long short-term memory (LSTM) neural network-based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM-based struc...
Neural Networks, Computer Unsupervised Machine Learning Humans
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Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation.

IEEE transactions on neural networks and learning systems Sep 13, 2019
Biomedical image segmentation is lately dominated by deep neural networks (DNNs) due to their surpassing expert-level performance. However, the existing DNN models for biomedical image segmentation are generally highly parameterized, which severely i...
Data Compression Humans Neurons Retinal Vessels Tomography, X-Ray Computed Image Processing, Computer-Assisted Neural Networks, Computer
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SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems Sep 9, 2019
Group convolution is widely used in many mobile networks to remove the filter's redundancy from the channel extent. In order to further reduce the redundancy of group convolution, this article proposes a novel repeated group convolutional (RGC) kerne...
Databases, Factual Computers, Handheld Neural Networks, Computer
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Backpropagation With N -D Vector-Valued Neurons Using Arbitrary Bilinear Products.

IEEE transactions on neural networks and learning systems Sep 6, 2019
Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of scalars; however, its performance may not be optimal since associations...
Neurons Support Vector Machine Humans Deep Learning Neural Networks, Computer
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Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning.

IEEE transactions on neural networks and learning systems Sep 5, 2019
Accurate segmentation of anatomical brain structures is crucial for many neuroimaging applications, e.g., early brain development studies and the study of imaging biomarkers of neurodegenerative diseases. Although multi-atlas segmentation (MAS) has a...
Image Interpretation, Computer-Assisted Deep Learning Humans Brain Mapping Databases, Factual Brain Neural Networks, Computer
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Barrier Function-Based Adaptive Control for Uncertain Strict-Feedback Systems Within Predefined Neural Network Approximation Sets.

IEEE transactions on neural networks and learning systems Sep 4, 2019
In this article, a globally stable adaptive control strategy for uncertain strict-feedback systems is proposed within predefined neural network (NN) approximation sets, despite the presence of unknown system nonlinearities. In contrast to the convent...
Nonlinear Dynamics Uncertainty Feedback Neural Networks, Computer
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Nonpooling Convolutional Neural Network Forecasting for Seasonal Time Series With Trends.

IEEE transactions on neural networks and learning systems Sep 4, 2019
This article focuses on a problem important to automatic machine learning: the automatic processing of a nonpreprocessed time series. The convolutional neural network (CNN) is one of the most popular neural network (NN) algorithms for pattern recogni...
Machine Learning Forecasting Neural Networks, Computer Humans Time Factors Seasons Databases, Factual
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Neural Probabilistic Graphical Model for Face Sketch Synthesis.

IEEE transactions on neural networks and learning systems Sep 4, 2019
Neural network learning for face sketch synthesis from photos has attracted substantial attention due to its favorable synthesis performance. However, most existing deep-learning-based face sketch synthesis models stacked only by multiple convolution...
Pattern Recognition, Automated Humans Probability Automated Facial Recognition Neural Networks, Computer
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Exploring Duality in Visual Question-Driven Top-Down Saliency.

IEEE transactions on neural networks and learning systems Sep 2, 2019
Top-down, goal-driven visual saliency exerts a huge influence on the human visual system for performing visual tasks. Text generations, like visual question answering (VQA) and visual question generation (VQG), have intrinsic connections with top-dow...
Humans Pattern Recognition, Automated Visual Perception Attention Neural Networks, Computer
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