IEEE transactions on neural networks and learning systems
Jun 1, 2022
Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking. This article presents a systematic and comprehensive evaluation of unsuperv...
IEEE transactions on neural networks and learning systems
Jun 1, 2022
This work focuses on image anomaly detection by leveraging only normal images in the training phase. Most previous methods tackle anomaly detection by reconstructing the input images with an autoencoder (AE)-based model, and an underlying assumption ...
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Abnormal behaviors in industrial systems may be early warnings on critical events that may cause severe damages to facilities and security. Thus, it is important to detect abnormal behaviors accurately and timely. However, the anomaly detection probl...
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Differentiable neural computers (DNCs) extend artificial neural networks with an explicit memory without interference, thus enabling the model to perform classic computation tasks, such as graph traversal. However, such models are difficult to train,...
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Graph neural networks are receiving increasing attention as state-of-the-art methods to process graph-structured data. However, similar to other neural networks, they tend to suffer from a high computational cost to perform training. Reservoir comput...
IEEE transactions on neural networks and learning systems
Jun 1, 2022
The human brain has evolved to perform complex and computationally expensive cognitive tasks, such as audio-visual perception and object detection, with ease. For instance, the brain can recognize speech in different dialects and perform other cognit...
IEEE transactions on neural networks and learning systems
Jun 1, 2022
Accurate identification and localization of the vertebrae in CT scans is a critical and standard pre-processing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and mo...
IEEE transactions on neural networks and learning systems
Jun 1, 2022
This brief investigates the reachable set estimation problem of the delayed Markovian jump neural networks (NNs) with bounded disturbances. First, an improved reciprocally convex inequality is proposed, which contains some existing ones as its specia...
Computational intelligence and neuroscience
Jun 1, 2022
To accelerate the practical applications of artificial intelligence, this paper proposes a high efficient layer-wise refined pruning method for deep neural networks at the software level and accelerates the inference process at the hardware level on ...
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
May 31, 2022
INTRODUCTION: This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional neural networks model for the fully automatic segmentation of regional pharyngeal volume of interests (VOIs) in cone-beam computed tomography scans to compare t...