1 code implementation • 26 Oct 2021 • Yu-Ying Liu, Alexander Moreno, Maxwell A. Xu, Shuang Li, Jena C. McDaniel, Nancy C. Brady, Agata Rozga, Fuxin Li, Le Song, James M. Rehg
We solve the first challenge by reformulating the estimation problem as an equivalent discrete time-inhomogeneous hidden Markov model.
1 code implementation • 22 Aug 2020 • Yu-Ying Liu, J. Nathan Kutz, Steven L. Brunton
Our multiscale hierarchical time-stepping scheme provides important advantages over current time-stepping algorithms, including (i) circumventing numerical stiffness due to disparate time-scales, (ii) improved accuracy in comparison with leading neural-network architectures, (iii) efficiency in long-time simulation/forecasting due to explicit training of slow time-scale dynamics, and (iv) a flexible framework that is parallelizable and may be integrated with standard numerical time-stepping algorithms.
1 code implementation • 10 Apr 2020 • Yu-Ying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz
The performance gains of this adaptive multiscale architecture are illustrated through a sequence of numerical experiments on synthetic examples and real-world spatial-temporal data.
no code implementations • NeurIPS 2015 • Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time.