Search Results for author: Hannah K. Wayment-Steele

Found 4 papers, 2 papers with code

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

no code implementations17 Mar 2018 Hannah K. Wayment-Steele, Vijay S. Pande

We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.

Transferable neural networks for enhanced sampling of protein dynamics

no code implementations2 Jan 2018 Mohammad M. Sultan, Hannah K. Wayment-Steele, Vijay S. Pande

In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification that allows us to rapidly perform sampling in multiple related systems.

Variational Encoding of Complex Dynamics

2 code implementations23 Nov 2017 Carlos X. Hernández, Hannah K. Wayment-Steele, Mohammad M. Sultan, Brooke E. Husic, Vijay S. Pande

Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds.

Protein Folding Time Series

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