Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Latent Residual Video Prediction   Designing video prediction models that account for the inherent uncertainty
of the future is challenging. Most works in the literature are based on
stochastic image-autoregressive recurrent networks, which raises several
performance and applicability issues. An alternative is to use fully latent
temporal models which untie frame synthesis and temporal dynamics. However, no
such model for stochastic video prediction has been proposed in the literature
yet, due to design and training difficulties. In this paper, we overcome these
difficulties by introducing a novel stochastic temporal model whose dynamics
are governed in a latent space by a residual update rule. This first-order
scheme is motivated by discretization schemes of differential equations. It
naturally models video dynamics as it allows our simpler, more interpretable,
latent model to outperform prior state-of-the-art methods on challenging
datasets.

22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org