Improving Storage of Patterns in Recurrent Neural Networks: Clones Based Model and Architecture - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

Improving Storage of Patterns in Recurrent Neural Networks: Clones Based Model and Architecture

Abstract

Artificial neural networks are used in various domains like computer science and computer engineering for tasks like image processing, design of associative memories... The goal is to mimic the impressive brain ability to process or to memorize and retrieve information. Recently a new model of neural network has been proposed and has been applied to design associative memories. Even if this model seems to be really efficient, it suffers from many weaknesses. Some propositions have been made to address these problems but they are limited. In this paper, we propose a new concept in the field of binary neural networks to efficiently solve these problems while optimizing the cost of the architecture.
No file

Dates and versions

hal-01101580 , version 1 (09-01-2015)

Identifiers

  • HAL Id : hal-01101580 , version 1

Cite

Hugues Nono Wouafo, Cyrille Chavet, Philippe Coussy. Improving Storage of Patterns in Recurrent Neural Networks: Clones Based Model and Architecture. IEEE Int'l Symposium on Circuits & Systems (ISCAS), May 2015, Lisbonne, Portugal. ⟨hal-01101580⟩
212 View
0 Download

Share

Gmail Mastodon Facebook X LinkedIn More