Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Sparse Coding Using Optimized Linear Expansion of Thresholds   We address the problem of reconstructing sparse signals from noisy and
compressive measurements using a feed-forward deep neural network (DNN) with an
architecture motivated by the iterative shrinkage-thresholding algorithm
(ISTA). We maintain the weights and biases of the network links as prescribed
by ISTA and model the nonlinear activation function using a linear expansion of
thresholds (LET), which has been very successful in image denoising and
deconvolution. The optimal set of coefficients of the parametrized activation
is learned over a training dataset containing measurement-sparse signal pairs,
corresponding to a fixed sensing matrix. For training, we develop an efficient
second-order algorithm, which requires only matrix-vector product computations
in every training epoch (Hessian-free optimization) and offers superior
convergence performance than gradient-descent optimization. Subsequently, we
derive an improved network architecture inspired by FISTA, a faster version of
ISTA, to achieve similar signal estimation performance with about 50% of the
number of layers. The resulting architecture turns out to be a deep residual
network, which has recently been shown to exhibit superior performance in
several visual recognition tasks. Numerical experiments demonstrate that the
proposed DNN architectures lead to 3 to 4 dB improvement in the reconstruction
signal-to-noise ratio (SNR), compared with the state-of-the-art sparse coding
algorithms.

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The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph