Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

In recent years, attention-based models have achieved impressive performance in natural
language processing and computer vision applications by effectively capturing contextual …

Deep neural networks (DNN) are now dominating in the most challenging applications of
machine learning. As DNNs can have complex architectures with millions of trainable …

The increasing paradigm shift towards i ntermittent computing has made it possible to
intermittently execute d eep neural network (DNN) inference on edge devices powered by …

Surface crack detection is an integral part of infrastructure health surveys. This work
presents a transformative shift towards rapid and reliable data collection capabilities …

Neural network pruning, which can be divided into unstructured pruning and structured
pruning strategies, has been proven to be an efficient method to substantially reduce the …

The paper shows the results of the mathematical model development and the numerical
simulation of the oxygen vacancies, and the distribution of TiO, Ti2O3, and TiO2 oxides in …

To facilitate the deployment of convolutional neural networks on resource-limited devices,
neural network pruning, especially filter pruning, has been shown to be a promising …

The advent of deep learning techniques has shown promising improvement in flood
forecasting accuracy which are crucial for operating reservoir and mitigating flood damages …

To adapt to complex scenes and strict accuracy requirements, evolutions have unstoppably
occurred in current convolutional neural networks (CNNs). However, these evolutions bring …