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Physics > Chemical Physics

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

Abstract: As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the timescale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according to the variational approach to conformational dynamics. We additionally provide evidence that the VDE framework (Hern\'{a}ndez et al., 2017), which uses this autocorrelation loss along with a time-lagged reconstruction loss, obtains a variationally optimized latent coordinate in comparison with related loss functions. We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.
Subjects: Chemical Physics (physics.chem-ph); Learning (cs.LG); Biological Physics (physics.bio-ph); Machine Learning (stat.ML)
Cite as: arXiv:1803.06449 [physics.chem-ph]
  (or arXiv:1803.06449v1 [physics.chem-ph] for this version)

Submission history

From: Hannah Wayment-Steele [view email]
[v1] Sat, 17 Mar 2018 03:27:31 GMT (1517kb,D)