Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RNA Secondary Structure Prediction By Learning Unrolled Algorithms   In this paper, we propose an end-to-end deep learning model, called E2Efold,
for RNA secondary structure prediction which can effectively take into account
the inherent constraints in the problem. The key idea of E2Efold is to directly
predict the RNA base-pairing matrix, and use an unrolled algorithm for
constrained programming as the template for deep architectures to enforce
constraints. With comprehensive experiments on benchmark datasets, we
demonstrate the superior performance of E2Efold: it predicts significantly
better structures compared to previous SOTA (especially for pseudoknotted
structures), while being as efficient as the fastest algorithms in terms of
inference time.

17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg