3 papers
cs.LG2026
Fully tensorial approach to hypercomplex-valued neural networks
Agnieszka Niemczynowicz, RadosÅaw Antoni Kycia
A fully tensorial theoretical framework for hypercomplex-valued neural networks is presented. The proposed approach enables neural network architectures to operate on data defined…
cs.LG2024
KHNNs: hypercomplex neural networks computations via Keras using TensorFlow and PyTorch
Agnieszka Niemczynowicz, RadosÅaw Antoni Kycia
Neural networks used in computations with more advanced algebras than real numbers perform better in some applications. However, there is no general framework for constructing hype…
cs.NE2024
Hypercomplex neural network in time series forecasting of stock data
RadosÅaw Kycia, Agnieszka Niemczynowicz
The goal of this paper is to test three classes of neural network (NN) architectures based on four-dimensional (4D) hypercomplex algebras for time series prediction. We evaluate di…