Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Traversing within the Gaussian Typical Set: Differentiable
  Gaussianization Layers for Inverse Problems Augmented by Normalizing Flows   Generative networks such as normalizing flows can serve as a learning-based
prior to augment inverse problems to achieve high-quality results. However, the
latent space vector may not remain a typical sample from the desired
high-dimensional standard Gaussian distribution when traversing the latent
space during an inversion. As a result, it can be challenging to attain a
high-fidelity solution, particularly in the presence of noise and inaccurate
physics-based models. To address this issue, we propose to re-parameterize and
Gaussianize the latent vector using novel differentiable data-dependent layers
wherein custom operators are defined by solving optimization problems. These
proposed layers enforce an inversion to find a feasible solution within a
Gaussian typical set of the latent space. We tested and validated our technique
on an image deblurring task and eikonal tomography -- a PDE-constrained inverse
problem and achieved high-fidelity results.

29
The categories of this paper are: funct-an
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph