Knowledge of the hidden factors that determine particular system dynamics is
crucial for both explaining them and pursuing goal-directed, interventional
actions. The inference of these factors without supervision given time series
data remains an open challenge. Here, we focus on spatio-temporal processes,
including wave propagations and weather dynamics, and assume that universal
causes (e.g. physics) apply throughout space and time. We apply a novel
DIstributed, Spatio-Temporal graph Artificial Neural network Architecture,
DISTANA, which learns a generative model in such domains. DISTANA requires
fewer parameters, and yields more accurate predictions than temporal
convolutional neural networks and other related approaches on a 2D circular
wave prediction task. We show that DISTANA, when combined with a retrospective
latent state inference principle called active tuning, can reliably derive
hidden local causal factors. In a current weather prediction benchmark, DISTANA
infers our planet's land-sea mask solely by observing temperature dynamics and
uses the self inferred information to improve its own prediction of
temperature. We are convinced that the retrospective inference of latent states
in generative RNN architectures will play an essential role in future research
on causal inference and explainable systems.
%0 Generic
%1 karlbauer2020hidden
%A Karlbauer, Matthias
%A Menge, Tobias
%A Otte, Sebastian
%A Lensch, Hendrik P. A.
%A Scholten, Thomas
%A Wulfmeyer, Volker
%A Butz, Martin V.
%D 2020
%K climate climatechange climatemodeling spacialtemporal todo:read
%T Hidden Latent State Inference in a Spatio-Temporal Generative Model
%U http://arxiv.org/abs/2009.09823
%X Knowledge of the hidden factors that determine particular system dynamics is
crucial for both explaining them and pursuing goal-directed, interventional
actions. The inference of these factors without supervision given time series
data remains an open challenge. Here, we focus on spatio-temporal processes,
including wave propagations and weather dynamics, and assume that universal
causes (e.g. physics) apply throughout space and time. We apply a novel
DIstributed, Spatio-Temporal graph Artificial Neural network Architecture,
DISTANA, which learns a generative model in such domains. DISTANA requires
fewer parameters, and yields more accurate predictions than temporal
convolutional neural networks and other related approaches on a 2D circular
wave prediction task. We show that DISTANA, when combined with a retrospective
latent state inference principle called active tuning, can reliably derive
hidden local causal factors. In a current weather prediction benchmark, DISTANA
infers our planet's land-sea mask solely by observing temperature dynamics and
uses the self inferred information to improve its own prediction of
temperature. We are convinced that the retrospective inference of latent states
in generative RNN architectures will play an essential role in future research
on causal inference and explainable systems.
@misc{karlbauer2020hidden,
abstract = {Knowledge of the hidden factors that determine particular system dynamics is
crucial for both explaining them and pursuing goal-directed, interventional
actions. The inference of these factors without supervision given time series
data remains an open challenge. Here, we focus on spatio-temporal processes,
including wave propagations and weather dynamics, and assume that universal
causes (e.g. physics) apply throughout space and time. We apply a novel
DIstributed, Spatio-Temporal graph Artificial Neural network Architecture,
DISTANA, which learns a generative model in such domains. DISTANA requires
fewer parameters, and yields more accurate predictions than temporal
convolutional neural networks and other related approaches on a 2D circular
wave prediction task. We show that DISTANA, when combined with a retrospective
latent state inference principle called active tuning, can reliably derive
hidden local causal factors. In a current weather prediction benchmark, DISTANA
infers our planet's land-sea mask solely by observing temperature dynamics and
uses the self inferred information to improve its own prediction of
temperature. We are convinced that the retrospective inference of latent states
in generative RNN architectures will play an essential role in future research
on causal inference and explainable systems.},
added-at = {2021-02-16T11:51:17.000+0100},
author = {Karlbauer, Matthias and Menge, Tobias and Otte, Sebastian and Lensch, Hendrik P. A. and Scholten, Thomas and Wulfmeyer, Volker and Butz, Martin V.},
biburl = {https://www.bibsonomy.org/bibtex/25fa27c868257069941da095b442321ba/annakrause},
description = {2009.09823v1.pdf},
interhash = {5711e126980308d525abfb0445867e50},
intrahash = {5fa27c868257069941da095b442321ba},
keywords = {climate climatechange climatemodeling spacialtemporal todo:read},
note = {cite arxiv:2009.09823Comment: As submitted to the 35th conference of the Association for the Advancement of Artificial Intelligence (AAAI-21)},
timestamp = {2021-02-16T11:51:17.000+0100},
title = {Hidden Latent State Inference in a Spatio-Temporal Generative Model},
url = {http://arxiv.org/abs/2009.09823},
year = 2020
}