This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Robust optimization has been established as a leading methodology to approach decision
problems under uncertainty. To derive a robust optimization model, a central ingredient is to …

Networking is crucial for smart city projects nowadays, as it offers an environment where
people and things are connected. This paper presents a chronology of factors on the …

While the 5G is being rolled out in different industrial sectors, the 6G is expected to
implement data-driven ubiquitous machine learning for industrial information integration …

Deep neural networks have been successful in many predictive modeling tasks, such as
image and language recognition, where large neural networks are often used to obtain good …

Deep learning has received much attention lately due to the impressive empirical
performance achieved by training algorithms. Consequently, a need for a better theoretical …

Deep neural networks (DNNs) are widely studied in various applications. A DNN consists of
layers of neurons that compute affine combinations, apply nonlinear operations, and …

There has been a great deal of recent interest in binarized neural networks, especially
because of their explainability. At the same time, automatic differentiation algorithms such as …

Besides training, mathematical optimization is also used in deep learning to model and
solve formulations over trained neural networks for purposes such as verification …

Quantum annealing is a promising paradigm for building practical quantum computers.
Compared to other approaches, quantum annealing technology has been scaled up to a …