---
res:
  bibo_abstract:
  - In this work, the Gardner problem of inferring interactions and fields for an
    Ising neural network from given patterns under a local stability hypothesis is
    addressed under a dual perspective. By means of duality arguments, an integer
    linear system is defined whose solution space is the dual of the Gardner space
    and whose solutions represent mutually unstable patterns. We propose and discuss
    Monte Carlo methods in order to find and remove unstable patterns and uniformly
    sample the space of interactions thereafter. We illustrate the problem on a set
    of real data and perform ensemble calculation that shows how the emergence of
    phase dominated by unstable patterns can be triggered in a nonlinear discontinuous
    way.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Daniele
      foaf_name: De Martino, Daniele
      foaf_surname: De Martino
      foaf_workInfoHomepage: http://www.librecat.org/personId=3FF5848A-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-5214-4706
  bibo_doi: 10.1142/S0129183116500674
  bibo_issue: '6'
  bibo_volume: 27
  dct_date: 2016^xs_gYear
  dct_identifier:
  - UT:000377674800010
  dct_language: eng
  dct_publisher: World Scientific Publishing@
  dct_title: The dual of the space of interactions in neural network models@
...