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.
@article{arxiv.2009.09187,
title = {Inferring, Predicting, and Denoising Causal Wave Dynamics},
author = {Matthias Karlbauer and Sebastian Otte and Hendrik P. A. Lensch and Thomas Scholten and Volker Wulfmeyer and Martin V. Butz},
journal= {arXiv preprint arXiv:2009.09187},
year = {2020}
}
Comments
As accepted by the 29th International Conference on Artificial Neural Networks (ICANN20)