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Note: Variational Encoding of Protein Dynamics Benefits from Maximizing
  Latent Autocorrelation

Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation

17 March 2018
H. Wayment-Steele
Vijay S. Pande
    DRL
ArXivPDFHTML

Papers citing "Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation"

6 / 6 papers shown
Title
Transferable neural networks for enhanced sampling of protein dynamics
Transferable neural networks for enhanced sampling of protein dynamics
Mohammad M. Sultan
H. Wayment-Steele
Vijay S. Pande
DRL
61
101
0
02 Jan 2018
Molecular enhanced sampling with autoencoders: On-the-fly collective
  variable discovery and accelerated free energy landscape exploration
Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
Wei Chen
Andrew L. Ferguson
43
199
0
30 Dec 2017
Variational Encoding of Complex Dynamics
Variational Encoding of Complex Dynamics
Carlos X. Hernández
H. Wayment-Steele
Mohammad M. Sultan
B. Husic
Vijay S. Pande
AI4CE
64
140
0
23 Nov 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
AI4CE
BDL
148
360
0
30 Oct 2017
Variational cross-validation of slow dynamical modes in molecular
  kinetics
Variational cross-validation of slow dynamical modes in molecular kinetics
R. McGibbon
Vijay S. Pande
63
175
0
30 Jul 2014
A variational approach to modeling slow processes in stochastic
  dynamical systems
A variational approach to modeling slow processes in stochastic dynamical systems
Frank Noé
Feliks Nuske
80
294
0
29 Nov 2012
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