Abstract Binarized Neural Networks (BNNs) can significantly reduce the inference latency
and energy consumption in resource-constrained devices due to their pure-logical …

Malware detection plays a crucial role in cyber-security with the increase in malware growth
and advancements in cyber-attacks. Previously unseen malware which is not determined by …

Recent advances in unsupervised domain adaptation (UDA) show that transferable
prototypical learning presents a powerful means for class conditional alignment, which …

For many learning problems one may not have access to fine grained label information; eg,
an image can be labeled as husky, dog, or even animal depending on the expertise of the …

This paper presents a comprehensive forest mapping system using a customized drone
payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation …

Image classification and segmentation are common applications of deep learning to
radiology. While many tasks can be framed using either classification or segmentation …

Unsupervised domain adaptation (UDA) has been successfully applied to transfer
knowledge from a labeled source domain to target domains without their labels. Recently …

Automated performance metrics objectively measure surgeon performance during a robot-
assisted radical prostatectomy. Machine learning has demonstrated that automated …

To improve the throughput and energy efficiency of Deep Neural Networks (DNNs) on
customized hardware, lightweight neural networks constrain the weights of DNNs to be a …

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 …