Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Level Error-Resilient Neural Networks with Learning   The problem of neural network association is to retrieve a previously
memorized pattern from its noisy version using a network of neurons. An ideal
neural network should include three components simultaneously: a learning
algorithm, a large pattern retrieval capacity and resilience against noise.
Prior works in this area usually improve one or two aspects at the cost of the
third.
  Our work takes a step forward in closing this gap. More specifically, we show
that by forcing natural constraints on the set of learning patterns, we can
drastically improve the retrieval capacity of our neural network. Moreover, we
devise a learning algorithm whose role is to learn those patterns satisfying
the above mentioned constraints. Finally we show that our neural network can
cope with a fair amount of noise.

24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org