3 citations · 7 across the 7 of their papers we have counts for
7 papers
AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks
Yimin Yang, Siamak Mehrkanoon
Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper intr…
Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection
Yurong Chen, Hui Zhang, Yaonan Wang +2
Anomaly detection (AD) has been an active research area in various domains. Yet, the increasing data scale, complexity, and dimension turn the traditional methods into challenging.…
Deconvolution-and-convolution Networks
Yimin Yang, Wandong Zhang, Jonathan Wu +2
2D Convolutional neural network (CNN) has arguably become the de facto standard for computer vision tasks. Recent findings, however, suggest that CNN may not be the best option for…
Multi-Model Least Squares-Based Recomputation Framework for Large Data Analysis
Wandong Zhang, QM Jonathan Wu, Yimin Yang +3
Most multilayer least squares (LS)-based neural networks are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsu…
Deep Networks with Fast Retraining
Wandong Zhang, Yimin Yang, Jonathan Wu
Recent work [1] has utilized Moore-Penrose (MP) inverse in deep convolutional neural network (DCNN) learning, which achieves better generalization performance over the DCNN with a…
Non-iterative recomputation of dense layers for performance improvement of DCNN
Yimin Yang, Q. M. Jonathan Wu, Xiexing Feng +1
An iterative method of learning has become a paradigm for training deep convolutional neural networks (DCNN). However, utilizing a non-iterative learning strategy can accelerate th…
Pulling back error to the hidden-node parameter technology: Single-hidden-layer feedforward network without output weight
Yimin Yang, Q. M. Jonathan Wu, Guangbin Huang +1
According to conventional neural network theories, the feature of single-hidden-layer feedforward neural networks(SLFNs) resorts to parameters of the weighted connections and hidde…