12 citations · 14 across the 4 of their papers we have counts for
4 papers
Experimentally testable whole brain manifolds that recapitulate behavior
Gerald M Pao, Cameron Smith, Joseph Park +8
We propose an algorithm grounded in dynamical systems theory that generalizes manifold learning from a global state representation, to a network of local interacting manifolds term…
kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos
Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa +5
Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-series analysis framework. Despite its wide applicability, EDM was not scalable to large datasets due to its…
Empirical Mode Modeling: A data-driven approach to recover and forecast nonlinear dynamics from noisy data
Joseph Park, Gerald M Pao, Erik Stabenau +2
Data-driven, model-free analytics are natural choices for discovery and forecasting of complex, nonlinear systems. Methods that operate in the system state-space require either an…
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution
Wassapon Watanakeesuntorn, Keichi Takahashi, Kohei Ichikawa +5
Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework. The latest implementation of EDM, cppEDM, has only been used for small datasets due to compu…