Learning Simple Algorithms from Examples

@InProceedings{pmlr-v48-zaremba16,
  title = 	 {Learning Simple Algorithms from Examples},
  author = 	 {Zaremba, Wojciech and Mikolov, Tomas and Joulin, Armand and Fergus, Rob},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {421--429},
  year = 	 {2016},
  editor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {New York, New York, USA},
  month = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/zaremba16.pdf},
  url = 	 {https://proceedings.mlr.press/v48/zaremba16.html},
  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.}
}