Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dropout improves Recurrent Neural Networks for Handwriting Recognition   Recurrent neural networks (RNNs) with Long Short-Term memory cells currently
hold the best known results in unconstrained handwriting recognition. We show
that their performance can be greatly improved using dropout - a recently
proposed regularization method for deep architectures. While previous works
showed that dropout gave superior performance in the context of convolutional
networks, it had never been applied to RNNs. In our approach, dropout is
carefully used in the network so that it does not affect the recurrent
connections, hence the power of RNNs in modeling sequence is preserved.
Extensive experiments on a broad range of handwritten databases confirm the
effectiveness of dropout on deep architectures even when the network mainly
consists of recurrent and shared connections.

22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas