Visual learning of arithmetic operations

Yedid Hoshen, Shmuel Peleg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A simple Neural Network model is presented for endto- end visual learning of arithmetic operations from pictures of numbers. The input consists of two pictures, each showing a 7-digit number. The output, also a picture, displays the number showing the result of an arithmetic operation (e.g., addition or subtraction) on the two input numbers. The concepts of a number, or of an operator, are not explicitly introduced. This indicates that addition is a simple cognitive task, which can be learned visually using a very small number of neurons. Other operations, e.g., multiplication, were not learnable using this architecture. Some tasks were not learnable end-To-end (e.g., addition with Roman numerals), but were easily learnable once broken into two separate sub-Tasks: A perceptual Character Recognition and cognitive Arithmetic sub-Tasks. This indicates that while some tasks may be easily learnable end-To-end, other may need to be broken into sub-Tasks.

Original languageAmerican English
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
Pages3733-3739
Number of pages7
ISBN (Electronic)9781577357605
StatePublished - 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States
CityPhoenix
Period12/02/1617/02/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Visual learning of arithmetic operations'. Together they form a unique fingerprint.

Cite this