Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Empirically Accelerating Scaled Gradient Projection Using Deep Neural
  Network For Inverse Problems In Image Processing   Recently, deep neural networks (DNNs) have shown advantages in accelerating
optimization algorithms. One approach is to unfold finite number of iterations
of conventional optimization algorithms and to learn parameters in the
algorithms. However, these are forward methods and are indeed neither iterative
nor convergent. Here, we present a novel DNN-based convergent iterative
algorithm that accelerates conventional optimization algorithms. We train a DNN
to yield parameters in scaled gradient projection method. So far, these
parameters have been chosen heuristically, but have shown to be crucial for
good empirical performance. In simulation results, the proposed method
significantly improves the empirical convergence rate over conventional
optimization methods for various large-scale inverse problems in image
processing.

33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph