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Variational approach for learning Markov processes from time series data

14 July 2017

Frank Noé

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

26 / 26 papers shown

Title
Koopman-Equivariant Gaussian Processes Petar Bevanda Max Beier Armin Lederer A. Capone Stefan Sosnowski Sandra Hirche AI4TS 110 2 0 10 Feb 2025
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups Vladimir Kostic Karim Lounici Helene Halconruy Timothée Devergne P. Novelli Massimiliano Pontil 73 0 0 18 Oct 2024
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck Marco Federici Patrick Forré Ryota Tomioka Bastiaan S. Veeling 53 6 0 13 Sep 2023
Reaction coordinate flows for model reduction of molecular kinetics Hao Wu Frank Noé 99 12 0 11 Sep 2023
Enhanced Sampling with Machine Learning: A Review S. Mehdi Zachary Smith Lukas Herron Ziyue Zou P. Tiwary AI4CE 52 8 0 15 Jun 2023
Transfer operators on graphs: Spectral clustering and beyond Stefan Klus Maia Trower 75 5 0 19 May 2023
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules Mahdi Ghorbani Samarjeet Prasad Jeffery B. Klauda B. Brooks GNN 49 32 0 12 Jan 2022
Deeptime: a Python library for machine learning dynamical models from time series data Moritz Hoffmann Martin K. Scherer Tim Hempel Andreas Mardt Brian M. de Silva ... Stefan Klus Hao Wu N. Kutz Steven L. Brunton Frank Noé AI4CE 101 107 0 28 Oct 2021
A purely data-driven framework for prediction, optimization, and control of networked processes: application to networked SIS epidemic model A. Tavasoli T. Henry Heman Shakeri 81 4 0 01 Aug 2021
A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer Wenqi Zeng Siqin Cao Xuhui Huang Yuan Yao 50 9 0 14 Jul 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 156 423 0 24 Feb 2021
Molecular Latent Space Simulators Hythem Sidky Wei Chen Andrew L. Ferguson AI4CE 64 34 0 01 Jul 2020
Kernel-based approximation of the Koopman generator and Schrödinger operator Stefan Klus Feliks Nuske B. Hamzi 57 59 0 27 May 2020
Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence Mattes Mollenhauer Stefan Klus Christof Schütte P. Koltai 73 9 0 02 Apr 2020
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems Sebastian Kaltenbach P. Koutsourelakis AI4CE 205 35 0 30 Dec 2019
Machine learning for protein folding and dynamics Frank Noé Gianni De Fabritiis C. Clementi AI4CE 121 138 0 22 Nov 2019
Machine learning for molecular simulation Frank Noé A. Tkatchenko K. Müller C. Clementi AI4CE 90 668 0 07 Nov 2019
Tensor-based computation of metastable and coherent sets Feliks Nuske Patrick Gelß Stefan Klus C. Clementi 32 13 0 12 Aug 2019
Kernel methods for detecting coherent structures in dynamical data Stefan Klus B. Husic Mattes Mollenhauer Frank Noé 30 29 0 16 Apr 2019
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials T. Xie A. France-Lanord Yanming Wang Y. Shao-horn Jeffrey C. Grossman AI4CE 50 111 0 18 Feb 2019
Machine Learning for Molecular Dynamics on Long Timescales Frank Noé AI4CE 77 32 0 18 Dec 2018
Variational Selection of Features for Molecular Kinetics Martin K. Scherer B. Husic Moritz Hoffmann Fabian Paul Hao Wu Frank Noé 96 50 0 28 Nov 2018
Deep Generative Markov State Models Hao Wu Andreas Mardt Luca Pasquali Frank Noe AI4CE 53 60 0 19 May 2018
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces Stefan Klus Ingmar Schuster Krikamol Muandet 93 122 0 05 Dec 2017
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 179 362 0 30 Oct 2017
VAMPnets: Deep learning of molecular kinetics Andreas Mardt Luca Pasquali Hao Wu Frank Noé 83 551 0 16 Oct 2017