Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Overparameterized networks trained to convergence have shown impressive performance
in domains such as computer vision and natural language processing. Pushing state of the …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

Deep learning (DL) models are computationally expensive in space and time, which makes
it difficult to deploy DL models in edge computing devices, such as Raspberry-Pi or Jetson …

The most common and aggressive malignant brain tumor in adults is glioma, which leads to
short life expectancy. A reliable and efficient automatic segmentation method is beneficial for …

In this work, we consider the problem of instance-wise dynamic network model selection for
multi-task learning. To this end, we propose an efficient approach to exploit a compact but …

As blockchain technology increasingly underpins digital transactions, smart contracts have
emerged as a pivotal tool for automating these transactions. While smart contracts offer …

In a world where security issues have been gaining growing importance, face recognition
systems have attracted increasing attention in multiple application areas, ranging from …