AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 511 to 520 of 796 articles

Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons.

IEEE transactions on neural networks and learning systems Nov 30, 2020
This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network ...
Computer Simulation Models, Neurological Pattern Recognition, Automated Electronic Data Processing Neurons Algorithms Databases, Factual Neural Networks, Computer Artificial Intelligence Humans
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Why ResNet Works? Residuals Generalize.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Residual connections significantly boost the performance of deep neural networks. However, few theoretical results address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks. This article st...
Deep Learning Generalization, Psychological Neural Networks, Computer Algorithms
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An Accelerated Finite-Time Convergent Neural Network for Visual Servoing of a Flexible Surgical Endoscope With Physical and RCM Constraints.

IEEE transactions on neural networks and learning systems Nov 30, 2020
This article designs and analyzes a recurrent neural network (RNN) for the visual servoing of a flexible surgical endoscope. The flexible surgical endoscope is based on a commercially available UR5 robot with a flexible endoscope attached as an end-e...
Reproducibility of Results Endoscopy Biomechanical Phenomena Neural Networks, Computer Algorithms Mechanical Phenomena Endoscopes Robotic Surgical Procedures Vision, Ocular Robotics Equipment Design
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PID Controller-Based Stochastic Optimization Acceleration for Deep Neural Networks.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Deep neural networks (DNNs) are widely used and demonstrated their power in many applications, such as computer vision and pattern recognition. However, the training of these networks can be time consuming. Such a problem could be alleviated by using...
Stochastic Processes Visual Prosthesis Deep Learning Natural Language Processing Algorithms Neural Networks, Computer Databases, Factual
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DACH: Domain Adaptation Without Domain Information.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video fe...
Neural Networks, Computer Deep Learning Data Interpretation, Statistical Image Interpretation, Computer-Assisted Machine Learning Algorithms
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Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems Nov 13, 2019
For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, theref...
Brain Female Young Adult Adult Reproducibility of Results Brain-Computer Interfaces Bayes Theorem Information Theory Algorithms Movement Electroencephalography Humans Databases, Factual Imagination Male Neural Networks, Computer Deep Learning
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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...
Pattern Recognition, Automated Artificial Intelligence Deep Learning Humans 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...
Humans Unsupervised Machine Learning Neural Networks, Computer
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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...
Humans Image Processing, Computer-Assisted Data Compression Neural Networks, Computer Neurons Retinal Vessels Tomography, X-Ray Computed
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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...
Neural Networks, Computer Databases, Factual Computers, Handheld
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