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Nearly-optimal associative memories based on distributed constant weight codes

A new family of sparse neural networks achieving nearly optimal performance has been recently introduced. In these networks, messages are stored as cliques in clustered graphs. In this paper, we interpret these networks using the formalism of error correcting codes. To achieve this, we introduce two original codes, the thrifty code and the clique code, that are both sub-families of binary constant weight codes. We also provide the networks with an enhanced retrieving rule that enables a property of answer correctness and that improves performance.

I thank Pascal Vontobel for pointing out several mistakes in the article (this version is updated).

@inproceedings{GriBer20122,
  author = {Vincent Gripon and Claude Berrou},
  title = {Nearly-optimal associative memories based
on distributed constant weight codes},
  booktitle = {Proceedings of Information Theory and
Applications Workshop},
  year = {2012},
  address = {San Diego, CA, USA},
  pages = {269--273},
  month = {February},
}