In recent years, a specific machine learning method called deep learning has gained huge
attraction, as it has obtained astonishing results in broad applications such as pattern …

With the breakthroughs in deep learning and contactless sensors, the recent years have
witnessed a rise of ambient intelligence applications and services, spanning from healthcare …

Compared to the traditional machine learning models, deep neural networks (DNN) are
known to be highly sensitive to the choice of hyperparameters. While the required time and …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many
signal processing and machine learning (ML) applications. It is used for solving optimization …

We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

The book series Modeling and Optimization in Science and Technologies (MOST) publishes
basic principles as well as novel theories and methods in the fast-evolving field of modeling …

Deep learning has shown that learned functions can dramatically outperform hand-designed
functions on perceptual tasks. Analogously, this suggests that learned optimizers may …

Deep learning (DL) is playing an increasingly important role in our lives. It has already made
a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars …

When and why can a neural network be successfully trained? This article provides an
overview of optimization algorithms and theory for training neural networks. First, we discuss …