Fully tensorial approach to hypercomplex neural networks

The fast progress in applications of Artificial Neural Networks (NN) promotes new directions of research and generalizations. This involves advanced mathematical concepts such as group theory [19], differential geometry [5, 6], or topological methods in data analysis [7]. The core of NN implementations lies in linear algebra usage.