Learning simple algorithms from examples

Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using Q-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by Q-learning.

Original languageEnglish (US)
Title of host publication33rd International Conference on Machine Learning, ICML 2016
EditorsMaria Florina Balcan, Kilian Q. Weinberger
PublisherInternational Machine Learning Society (IMLS)
Pages639-647
Number of pages9
ISBN (Electronic)9781510829008
StatePublished - Jan 1 2016
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: Jun 19 2016Jun 24 2016

Publication series

Name33rd International Conference on Machine Learning, ICML 2016
Volume1

Other

Other33rd International Conference on Machine Learning, ICML 2016
CountryUnited States
CityNew York City
Period6/19/166/24/16

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications

Cite this

Zaremba, W., Mikolov, T., Joulin, A., & Fergus, R. (2016). Learning simple algorithms from examples. In M. F. Balcan, & K. Q. Weinberger (Eds.), 33rd International Conference on Machine Learning, ICML 2016 (pp. 639-647). (33rd International Conference on Machine Learning, ICML 2016; Vol. 1). International Machine Learning Society (IMLS).