218 citations · 333 across the 6 of their papers we have counts for
7 papers
Optimal Scheduling of Anticipated COVID-19 Vaccination: A Case Study of New York State
Syed Irfan Ali Meerza, Seyed M. Karimi, Bert B. Little +2
This study aims to determine an optimal control strategy for vaccine scheduling in COVID-19 pandemic treatment by converting widely acknowledged infectious disease model named SEIR…
On Correlation of Features Extracted by Deep Neural Networks
Babajide O. Ayinde, Tamer Inanc, Jacek M. Zurada
Redundancy in deep neural network (DNN) models has always been one of their most intriguing and important properties. DNNs have been shown to overparameterize, or extract a lot of…
Diversity Regularized Adversarial Learning
Babajide O. Ayinde, Keishin Nishihama, Jacek M. Zurada
The two key players in Generative Adversarial Networks (GANs), the discriminator and generator, are usually parameterized as deep neural networks (DNNs). On many generative tasks,…
Building Efficient ConvNets using Redundant Feature Pruning
Babajide O. Ayinde, Jacek M. Zurada
This paper presents an efficient technique to prune deep and/or wide convolutional neural network models by eliminating redundant features (or filters). Previous studies have shown…
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data
Babajide O. Ayinde, Jacek M. Zurada
Unsupervised feature extractors are known to perform an efficient and discriminative representation of data. Insight into the mappings they perform and human ability to understand…
Structured Sparse Convolutional Autoencoder
Ehsan Hosseini-Asl
This paper aims to improve the feature learning in Convolutional Networks (Convnet) by capturing the structure of objects. A new sparsity function is imposed on the extracted featu…
Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints
Ehsan Hosseini-Asl, Jacek M. Zurada, Olfa Nasraoui
We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. T…