The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate. In recent years, convolutional neural network has been widely used to identify the h...
Neural networks : the official journal of the International Neural Network Society
36525915
Recurrent Neural Network (RNN) models have been applied in different domains, producing high accuracies on time-dependent data. However, RNNs have long suffered from exploding gradients during training, mainly due to their recurrent process. In this ...
Neural networks : the official journal of the International Neural Network Society
36436301
We consider synchronous data-parallel neural network training with a fixed large batch size. While the large batch size provides a high degree of parallelism, it degrades the generalization performance due to the low gradient noise scale. We propose ...
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from gen...
Neural networks : the official journal of the International Neural Network Society
36758464
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the ...
Neural networks : the official journal of the International Neural Network Society
36827960
Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The two independent branches of DR are feature selection (FS) and feature projection (FP). FS focuses on select...
Neural networks : the official journal of the International Neural Network Society
36805266
Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large dat...
Neural networks : the official journal of the International Neural Network Society
36774871
The class of multi-relational graph convolutional networks (MRGCNs) is a recent extension of standard graph convolutional networks (GCNs) to handle heterogenous graphs with multiple types of relationships. MRGCNs have been shown to yield results supe...
Neural networks : the official journal of the International Neural Network Society
36774869
The state of the art in violence detection in videos has improved in recent years thanks to deep learning models, but it is still below 90% of average precision in the most complex datasets, which may pose a problem of frequent false alarms in video ...
Neural networks : the official journal of the International Neural Network Society
36774864
Matrix factorization has always been an encouraging field, which attempts to extract discriminative features from high-dimensional data. However, it suffers from negative generalization ability and high computational complexity when handling large-sc...