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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
Hao Wu
Frank Noé
    BDLAI4TS
ArXiv (abs)PDFHTML

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
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
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
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
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
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
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
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 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
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
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
Molecular Latent Space Simulators
Hythem Sidky
Wei Chen
Andrew L. Ferguson
AI4CE
74
34
0
01 Jul 2020
Kernel-based approximation of the Koopman generator and Schrödinger
  operator
Kernel-based approximation of the Koopman generator and Schrödinger operator
Stefan Klus
Feliks Nuske
B. Hamzi
70
59
0
27 May 2020
Kernel Autocovariance Operators of Stationary Processes: Estimation and
  Convergence
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
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
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
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
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
Kernel methods for detecting coherent structures in dynamical data
Stefan Klus
B. Husic
Mattes Mollenhauer
Frank Noé
36
29
0
16 Apr 2019
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale
  Dynamics in Materials
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
56
111
0
18 Feb 2019
Machine Learning for Molecular Dynamics on Long Timescales
Machine Learning for Molecular Dynamics on Long Timescales
Frank Noé
AI4CE
77
32
0
18 Dec 2018
Variational Selection of Features for Molecular Kinetics
Variational Selection of Features for Molecular Kinetics
Martin K. Scherer
B. Husic
Moritz Hoffmann
Fabian Paul
Hao Wu
Frank Noé
111
50
0
28 Nov 2018
Deep Generative Markov State Models
Deep Generative Markov State Models
Hao Wu
Andreas Mardt
Luca Pasquali
Frank Noe
AI4CE
57
60
0
19 May 2018
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert
  Spaces
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
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
C. Wehmeyer
Frank Noé
AI4CEBDL
179
362
0
30 Oct 2017
VAMPnets: Deep learning of molecular kinetics
VAMPnets: Deep learning of molecular kinetics
Andreas Mardt
Luca Pasquali
Hao Wu
Frank Noé
105
551
0
16 Oct 2017
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