Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Designing neural networks that process mean values of random variables   We introduce a class of neural networks derived from probabilistic models in
the form of Bayesian networks. By imposing additional assumptions about the
nature of the probabilistic models represented in the networks, we derive
neural networks with standard dynamics that require no training to determine
the synaptic weights, that perform accurate calculation of the mean values of
the random variables, that can pool multiple sources of evidence, and that deal
cleanly and consistently with inconsistent or contradictory evidence. The
presented neural networks capture many properties of Bayesian networks,
providing distributed versions of probabilistic models.

7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat