Abstract: This chapter contains sections titled: Artificial Neural Networks, Neural Network Learning Algorithms, What a Perceptron Can and Cannot Do, Connectionist Models in Cognitive Science, Neural ...

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically ...

Abstract: The current optical convolution architectures are facing challenges related to limited scalability, excessive data redundancy and restricted processing bandwidth. In this work, we introduce ...

Abstract: In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models ...

Abstract: Due to the sharing of communication and sensing signals, integrated sensing and communication (ISAC) systems are vulnerable to potential eavesdropping attacks. This article investigates ...

Book Abstract: Electrical Engineering/Electromagnetics Waves and Fields in Inhomogeneous Media A Volume in the IEEE Press Series on Electromagnetic Waves Donald G ...

Abstract: Two machine-learning procedures have been investigated in some detail using the game of checkers. Enough work has been done to verify the fact that a computer can be programmed so that it ...

This chapter contains sections titled: ...

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it ...

Metamaterials: Physics and Engineering Explorations gives readers a clearly written, richly illustrated introduction to the most recent research developments in the area of electromagnetic ...

Book Abstract: "Co-published with Oxford University Press Long considered the most comprehensive account of electromagnetic theory and analytical methods for solving waveguide and cavity problems, ...