FMM-Net: neural network architecture based on the Fast Multipole Method
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 44 references that are in the map. First posted on arXiv 25 Dec 2022.
Builds on
6 of 44 references.
References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
Built on by
Citing papers per year: 2026: 1.
1 paper.