Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Processing of missing data by neural networks   We propose a general, theoretically justified mechanism for processing
missing data by neural networks. Our idea is to replace typical neuron's
response in the first hidden layer by its expected value. This approach can be
applied for various types of networks at minimal cost in their modification.
Moreover, in contrast to recent approaches, it does not require complete data
for training. Experimental results performed on different types of
architectures show that our method gives better results than typical imputation
strategies and other methods dedicated for incomplete data.

25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom