Name
Abstract | ||
|---|---|---|
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally parameterizable laterally connected network modules. DISTANA is specifically designed to identify the causality behind spatially distributed, non-linear dynamical processes. We show that DISTANA is very well-suited to denoise data streams, given that re-occurring patterns are observed, significantly outperforming alternative approaches, such as temporal convolution networks and ConvLSTMs, on a complex spatial wave propagation benchmark. It produces stable and accurate closed-loop predictions even over hundreds of time steps. Moreover, it is able to effectively filter noise -- an ability that can be improved further by applying denoising autoencoder principles or by actively tuning latent neural state activities retrospectively. Results confirm that DISTANA is ready to model real-world spatio-temporal dynamics such as brain imaging, supply networks, water flow, or soil and weather data patterns. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-61609-0_45 | ICANN (1) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Karlbauer | 1 | 0 | 1.01 |
| Sebastian Otte | 2 | 47 | 12.57 |
| Hendrik P. A. Lensch | 3 | 1471 | 96.59 |
| Thomas Scholten | 4 | 0 | 0.34 |
| volker wulfmeyer | 5 | 0 | 2.37 |
| Martin V. Butz | 6 | 1065 | 85.21 |