Vec2Node: Self-Training with Tensor Augmentation for Text Classification with Few Labels

TL;DR: It is demonstrated that neural networks are capable of creating additional samples for their own training, thus increasing their approximating efficiency, and having passed through the stages of the algorithm, artificial neural networks significantly improve their interpolation performance in long-term forecasting.

TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.

TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.

TL;DR: This paper proposed a new approach based on skip-gram model, where each word is represented as a bag of character n-grams, words being represented as the sum of these representations, allowing to train models on large corpora quickly and allowing to compute word representations for words that did not appear in the training data.