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 …

Abstract Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs becomes …

Causal discovery methods typically extract causal relations between multiple nodes
(variables) based on univariate observations of each node. However, one frequently …

Abstract Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term
Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved …

Low-rank tensor factorization has been widely used for many real world tensor completion
problems. While most existing factorization models assume a multilinearity relationship …

Tensor train decomposition is a powerful representation for high-order tensors, which has
been successfully applied to various machine learning tasks in recent years. In this paper …

Recommender systems often involve multi-aspect factors. For example, when shop** for
shoes online, consumers usually look through their images, ratings, and product's reviews …

We consider the problem of decomposing a higher-order tensor with binary entries. Such
data problems arise frequently in applications such as neuroimaging, recommendation …

Modern healthcare systems knitted by a web of entities (eg, hospitals, clinics, pharmacy
companies) are collecting a huge volume of healthcare data from a large number of …

Deep neural networks (DNNs) have achieved outstanding performance in a wide range of
applications, eg, image classification, natural language processing, etc. Despite the good …