Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …

Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …

Multitask learning is a common approach in machine learning, which allows to train multiple
objectives with a shared architecture. It has been shown that by training multiple tasks …

High-velocity data streams present a challenge to deep learning-based computer vision
models due to the resources needed to retrain for new incremental data. This study presents …

Fault detection is a crucial task in seismic structure interpretation. Convolutional neural
network (CNN)-based methods, in general, require large amount of labeled data for network …

Driven by deep learning techniques and large-scale datasets, recent years have witnessed
a paradigm shift in automatic lip reading. While the main thrust of Visual Speech …

Over the last decade, advances in Machine Learning and Artificial Intelligence have
highlighted their potential as a diagnostic tool in the healthcare domain. Despite the …

Chest X-rays (CXRs) are a rich source of information for physicians–essential for disease
diagnosis and treatment selection. Recent deep learning models aim to alleviate strain on …

Early Exit Neural Networks (EENNs) endow neural network architectures with auxiliary
classifiers to progressively process the input and make decisions at intermediate points of …

While automated driving is often advertised with better-than-human driving performance, this
chapter reviews that it is nearly impossible to provide direct statistical evidence on the …