Feb 1, 2023 · This paper proposes GRAPHS4MER, a spatiotemporal model that combines S4 with graph structure learning to model long-range time series data. The ...
Nov 21, 2022 · To address these challenges, we propose representing multivariate biosignals as time-dependent graphs and introduce GraphS4mer, a general graph ...
May 14, 2023 · Specifically, (1) we leverage Structured State Spaces model (S4), a state-of-the-art sequence model, to capture long-term temporal dependencies ...
This work proposes representing multivariate signals as graphs as graphs and introduces GraphS4mer, a general graph neural network (GNN) architecture that ...
[PDF] Modeling Multivariate Biosignals With Graph Neural Networks and ...
proceedings.mlr.press › ...
In this study, we address the foregoing challenges by (1) leveraging S4 to enable long-range tempo- ral modeling in biosignals, (2) proposing a graph. 51. Page ...
Modeling Multivariate Biosignals with Graph Neural Networks and Structured State Space ... models spatiotemporal dependencies in multivariate biosignals ...
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models. Paper and Code. View Code Notebook.
Nov 21, 2022 · Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models ... Structured State Spaces model ...
People also ask
What are spatio temporal graphs?
What is spatial graph neural network?
Jan 11, 2024 · ... spatiotemporal graph neural networks (STGNNs) to model spatial and temporal data correlations. ... Koopman, Time series analysis by state space ...
MODELING MULTIVARIATE BIOSIGNALS WITH GRAPH. NEURAL NETWORKS AND STRUCTURED STATE SPACE. Siyi Tang†, Jared Dunnmon†, Liangqiong Qu‡, Khaled Saab†, Tina ...