Variational approach for learning Markov processes from time series data

v1v2v3 (latest)

Variational approach for learning Markov processes from time series data

14 July 2017

Frank Noé

ArXiv (abs)PDF HTML

Papers citing "Variational approach for learning Markov processes from time series data"

9 / 9 papers shown

Title
Deep learning for universal linear embeddings of nonlinear dynamics Bethany Lusch J. Nathan Kutz Steven L. Brunton 81 1,260 0 27 Dec 2017
Linearly-Recurrent Autoencoder Networks for Learning Dynamics Samuel E. Otto C. Rowley AI4CE 58 328 0 04 Dec 2017
VAMPnets: Deep learning of molecular kinetics Andreas Mardt Luca Pasquali Hao Wu Frank Noé 62 546 0 16 Oct 2017
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition Naoya Takeishi Yoshinobu Kawahara Takehisa Yairi 49 374 0 12 Oct 2017
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations Hao Wu Feliks Nuske Fabian Paul Stefan Klus P. Koltai Frank Noé 134 128 0 20 Oct 2016
Variational cross-validation of slow dynamical modes in molecular kinetics R. McGibbon Vijay S. Pande 78 176 0 30 Jul 2014
A variational approach to modeling slow processes in stochastic dynamical systems Frank Noé Feliks Nuske 84 295 0 29 Nov 2012
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 376 7,965 0 13 Jun 2012
A survey of cross-validation procedures for model selection Sylvain Arlot Alain Celisse 216 3,598 0 27 Jul 2009