Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative
  Models   A promising class of generative models maps points from a simple distribution
to a complex distribution through an invertible neural network.
Likelihood-based training of these models requires restricting their
architectures to allow cheap computation of Jacobian determinants.
Alternatively, the Jacobian trace can be used if the transformation is
specified by an ordinary differential equation. In this paper, we use
Hutchinson's trace estimator to give a scalable unbiased estimate of the
log-density. The result is a continuous-time invertible generative model with
unbiased density estimation and one-pass sampling, while allowing unrestricted
neural network architectures. We demonstrate our approach on high-dimensional
density estimation, image generation, and variational inference, achieving the
state-of-the-art among exact likelihood methods with efficient sampling.

23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio