19 citations · 36 across the 6 of their papers we have counts for
6 papers
Breaking Time Invariance: Assorted-Time Normalization for RNNs
Cole Pospisil, Vasily Zadorozhnyy, Qiang Ye
Methods such as Layer Normalization (LN) and Batch Normalization (BN) have proven to be effective in improving the training of Recurrent Neural Networks (RNNs). However, existing m…
Orthogonal Gated Recurrent Unit with Neumann-Cayley Transformation
Edison Mucllari, Vasily Zadorozhnyy, Cole Pospisil +2
In recent years, using orthogonal matrices has been shown to be a promising approach in improving Recurrent Neural Networks (RNNs) with training, stability, and convergence, partic…
Symmetry Structured Convolutional Neural Networks
Kehelwala Dewage Gayan Maduranga, Vasily Zadorozhnyy, Qiang Ye
We consider Convolutional Neural Networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions. Such networks arise in modeling pairwise relationships fo…
Batch Normalization Preconditioning for Neural Network Training
Susanna Lange, Kyle Helfrich, Qiang Ye
Batch normalization (BN) is a popular and ubiquitous method in deep learning that has been shown to decrease training time and improve generalization performance of neural networks…
Two-stage short-term wind power forecasting algorithm using different feature-learning models
Jiancheng Qin, Jin Yang, Ying Chen +2
Two-stage ensemble-based forecasting methods have been studied extensively in the wind power forecasting field. However, deep learning-based wind power forecasting studies have not…
A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures
X. Yao, X. Li, Q. Ye +3
Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy. Manual seizure identification on…