Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Flexible Framework for Designing Trainable Priors with Adaptive
  Smoothing and Game Encoding   We introduce a general framework for designing and training neural network
layers whose forward passes can be interpreted as solving non-smooth convex
optimization problems, and whose architectures are derived from an optimization
algorithm. We focus on convex games, solved by local agents represented by the
nodes of a graph and interacting through regularization functions. This
approach is appealing for solving imaging problems, as it allows the use of
classical image priors within deep models that are trainable end to end. The
priors used in this presentation include variants of total variation, Laplacian
regularization, bilateral filtering, sparse coding on learned dictionaries, and
non-local self similarities. Our models are fully interpretable as well as
parameter and data efficient. Our experiments demonstrate their effectiveness
on a large diversity of tasks ranging from image denoising and compressed
sensing for fMRI to dense stereo matching.

17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
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The categories of this paper are: eess
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas