12 citations · 15 across the 6 of their papers we have counts for
6 papers
Group-Invariant Unsupervised Skill Discovery: Symmetry-aware Skill Representations for Generalizable Behavior
Junwoo Chang, Joseph Park, Roberto Horowitz +2
Unsupervised skill discovery aims to acquire behavior primitives that improve exploration and accelerate downstream task learning. However, existing approaches often ignore the geo…
Control of complex systems with generalized embedding and empirical dynamic modeling
Joseph Park, George Sugihara, Gerald Pao
Effective control requires knowledge of the process dynamics to guide the system toward desired states. In many control applications this knowledge is expressed mathematically or t…
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…