Applications of machine learning are subject to three major components that contribute to
the final performance metrics. Within the category of neural networks, and deep learning …

In this chapter we will first describe the basic HF approach, and then examine well-known
performance-improving techniques such as preconditioning which we have found to be …

Next-generation wireless communication networks will benefit from beamforming gain to
utilize higher bandwidths at millimeter wave (mmWave) and terahertz (THz) bands. For high …

As the complexity of neural network models has grown, it has become increasingly important
to optimize their design automatically through metalearning. Methods for discovering …

To satisfy the expected plethora of demanding services, the future generation of wireless
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …

Advanced technologies are required in future mobile wireless networks to support services
with highly diverse requirements in terms of high data rate and reliability, low latency, and …

This paper considers the design of optimal resource allocation policies in wireless
communication systems, which are generically modeled as a functional optimization …

With the advent of big data, deep learning technology has become an important research
direction in the field of machine learning, which has been widely applied in the image …

Machine learning is fast evolving, with numerous theoretical advances and applications in a
variety of domains. In reality, most machine learning algorithms are based on optimization …

Learned optimizers---neural networks that are trained to act as optimizers---have the
potential to dramatically accelerate training of machine learning models. However, even …