12 citations · 16 across the 6 of their papers we have counts for
6 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…
Global evidence for non-random dynamics in fish recruitment
Charles T. Perretti, Stephan B. Munch, Michael J. Fogarty +1
Understanding what controls apparently random fluctuations in fish recruitment is a major challenge in fisheries science. Our current inability to anticipate recruitment failures h…
Reply to Hartig et al. [arXiv:1305.3544]: The "true model" myth
Charles T. Perretti, Stephan B. Munch, George Sugihara
We have shown that commonly used model-fitting methods fail when the underlying ecological system is chaotic. Importantly, Hartig & Dormann [arXiv:1305.3544] suggest an alternative…