Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …

This article introduces two Tensor Network-based iterative algorithms for the identification of
high-order discrete-time nonlinear multiple-input multiple-output (MIMO) Volterra systems …

In this article, a new technique for macromodeling of high-frequency circuits and
components called high-order deep recurrent neural network (HODRNN) is proposed. This …

Many critical electronic design automation (EDA) problems suffer from the curse of
dimensionality, ie, the very fast-scaling computational burden produced by large number of …

This paper presents an extension of the Vector Fitting algorithm with the purpose of
constructing compact behavioral models of weakly nonlinear circuits starting from frequency …

Semiconductor optical amplifiers (SOAs) have found widespread use in optical
communications covering the important telecommunications window with optical carrier …

Cognitive maps are high level representations of the key topological attributes of real or
abstract spatial environments progressively built by a sequence of noisy observations …

Order reduction of Volterra and Volterra-Laguerre Models Page 1 Order Reduction of
Volterra and Volterra-Laguerre Models Mihai Telescu, Member, IEEE, Noël Tanguy, Member …

This article introduces two Tensor Train-based iterative algorithms for the identification of
high order discrete-time nonlinear MIMO Volterra systems. The system identification problem …

Active learning is a protocol for supervised machine learning, in which a learning algorithm
sequentially requests the labels of selected data points from a large pool of unlabeled data …