Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Note: Variational Encoding of Protein Dynamics Benefits from Maximizing
  Latent Autocorrelation   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\'andez 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.

5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th