Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CRNNTL: convolutional recurrent neural network and transfer learning for
  QSAR modelling   In this study, we propose the convolutional recurrent neural network and
transfer learning (CRNNTL) for QSAR modelling. The method was inspired by the
applications of polyphonic sound detection and electrocardiogram
classification. Our strategy takes advantages of both convolutional and
recurrent neural networks for feature extraction, as well as the data
augmentation method. Herein, CRNNTL is evaluated on 20 benchmark datasets in
comparison with baseline methods. In addition, one isomers based dataset is
used to elucidate its ability for both local and global feature extraction.
Then, knowledge transfer performance of CRNNTL is tested, especially for small
biological activity datasets. Finally, different latent representations from
other type of AEs were used for versatility study of our model. The results
show the effectiveness of CRNNTL using different latent representation.
Moreover, efficient knowledge transfer is achieved to overcome data scarcity
considering binding site similarity between different targets.

20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat