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Learning Arithmetic Operations With A Multistep Deep Learning

Bastien Nollet Mathieu Lefort 1 Frédéric Armetta 1 
1 SyCoSMA - Systèmes Cognitifs et Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Deep neural networks are difficult to train when applied to tasks that can be expressed as algorithmic procedures. In this article, we propose to study how the explicit guidance of a network through all steps of the algorithm, using external memory and active choice of inputs, can improve its learning capability. The idea is to take inspiration from a child's learning and running through a procedure via interaction with an external support such as a paper. We show that this mechanism applied to a simple multilayer perceptron can significantly improve its performance when learning either a multi-digit addition or multiplication, which are simple but yet challenging operations to learn.
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Submitted on : Thursday, October 22, 2020 - 4:39:23 PM
Last modification on : Sunday, June 26, 2022 - 2:55:02 AM
Long-term archiving on: : Saturday, January 23, 2021 - 7:51:48 PM


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Bastien Nollet, Mathieu Lefort, Frédéric Armetta. Learning Arithmetic Operations With A Multistep Deep Learning. The International Joint Conference on Neural Networks (IJCNN), Jul 2020, Glasgow, United Kingdom. ⟨10.1109/IJCNN48605.2020.9206963⟩. ⟨hal-02929738⟩



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