Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding   The learned iterative shrinkage thresholding algorithm (LISTA) introduces
deep unfolding models with learnable thresholds in some shrinkage functions for
sparse coding. Drawing on some theoretical insights, we advocate an error-based
thresholding (EBT) mechanism for LISTA, which leverages a function of the
layer-wise reconstruction error to suggest an appropriate threshold value for
each observation on each layer. We show that the EBT mechanism well
disentangles the learnable parameters in the shrinkage functions from the
reconstruction errors, making them more adaptive to the various observations.
With rigorous theoretical analyses, we show that the proposed EBT can lead to a
faster convergence on the basis of LISTA and its variants, in addition to its
higher adaptivity. Extensive experimental results confirm our theoretical
analyses and verify the effectiveness of our methods.

36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph