Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease
containment decisions. In this study, we demonstrate how transfer learning from deep …

Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural
Networks (DNN). It offers many significant opportunities for improving DNN performance and …

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to
downscale feature maps for larger receptive fields and less memory consumption. However …

Abstract Neural Architecture Search (NAS) aims to automatically discover optimal
architectures. In this paper, we propose an hourglass-inspired approach (HourNAS) for …

With the explosion in Big Data, it is often forgotten that much of the data nowadays is
generated at the edge. Specifically, a major source of data is users' endpoint devices like …

Indonesia is a country with diverse ethnic, religious, and cultural backgrounds. Among the
tribes in Indonesia, one of them is the Batak tribe. The Batak tribe has a variety of cultures …

The maximum classifier discrepancy method has achieved great success in solving
unsupervised domain adaptation tasks for image classification in recent years. Its basic …

Abstract Deep Neural Networks (DNNs) have been traditionally designed by human experts
in a painstaking and expensive process, dubbed by many researchers to be more of an art …

Abstract Deep Neural Networks (DNNs) have been adopted in many systems because of
their higher classification accuracy. While progress in achieving large scale, highly accurate …