Learning long sequences in binary neural networks - Archive ouverte HAL
Communication Dans Un Congrès Année : 2012

Learning long sequences in binary neural networks

Résumé

An original architecture of oriented sparse neural networks that enables the introduction of sequentiality in associative memories is proposed in this paper. This architecture can be regarded as a generalization of a recently proposed non oriented binary network based on cliques. Using a limited neuron resource, the network is able to learn very long sequences and to retrieve them only from the knowledge of some consecutive symbols.
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Dates et versions

hal-01056538 , version 1 (20-08-2014)

Identifiants

  • HAL Id : hal-01056538 , version 1

Citer

Xiaoran Jiang, Vincent Gripon, Claude Berrou. Learning long sequences in binary neural networks. Cognitive 2012 : 4th International Conference on Advanced Cognitive Technologies and Applications, Jul 2012, Nice, France. pp.165 - 170. ⟨hal-01056538⟩
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