Nov 21, 2022 · Specifically, (1) we leverage the Structured State Space architecture, a state-of-the-art deep sequence model, to capture long-range temporal ...

Specifically, (1) we leverage the Structured State Space architec- ture, a state-of-the-art deep sequence model, to capture long-range temporal dependencies in.

Apr 21, 2023 · Specifically, (1) we leverage the Structured State Spaces architecture, a state-of-the-art deep sequence model, to capture long-range temporal ...

Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models · Setup · Datasets · Model Training · Updates · Reference · About.

This work proposes and introduces GraphS4mer, a general graph neural network (GNN) architecture that improves performance on biosignal classification tasks ...

To address these challenges, we propose representing multivariate biosignals as time-dependent graphs and in- troduce GRAPHS4MER, a general graph neural network ...

2023-04: Our paper on modeling multivariate biosignals ... Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models (Best ...

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 ...

Diagonal State Spaces are as Effective as Structured State Spaces ... Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space ...