FMM-Net: neural network architecture based on the Fast Multipole Method

In this paper, we propose a new neural network architecture based on the H2 matrix. Even though networks with H2-inspired architecture already exist, and our approach is designed to reduce memory costs and improve performance by taking into account the sparsity template of the H2 matrix. In numerical comparison with alternative neural networks, including the known H2-based ones, our architecture showed itself as beneficial in terms of performance, memory, and scalability

Author Correction: Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task

An amendment to this paper has been published and can be accessed via a link at the top of the paper