Learning simple algorithms from examples

33rd International Conference on Machine Learning, ICML 2016

Volumn 1, Issue , 2016, Pages 639-647

  Zaremba, Wojciech ^a   Mikolov, Tomas ^b   Joulin, Armand ^b   Fergus, Rob ^b  

^a NEW YORK UNIVERSITY   (United States)

^b FACEBOOK AI RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONTROLLERS; LEARNING SYSTEMS;

INPUT AND OUTPUTS; Q-LEARNING; SIMPLE ALGORITHM; TEST EXAMPLES;

LEARNING ALGORITHMS;

EID: 84997831765     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Cho, Kyunghyun, van Merrienboer, Bart, Gulcehre, Caglar, Bougares, Fethi, Schwenk, Holger, and Bengio, Yoshua. Learning phrase representations using RNN encoderdecoder for statistical machine translation. arXiv preprint arXiv: 1406.1078, 2014.
2. Das, Sreerupa, Giles, C Lee, and Sun, Guo-Zheng. Learning context-free grammars: Capabilities and limitations of a recurrent neural network with an external stack memory. In In Proceedings of The Fourteenth Annual Conference of Cognitive Science Society, 1992.
3. Gers, Felix A, Schraudolph, Nicol N, and Schmidhuber, Jürgen. Learning precise timing with lstm recurrent networks. The Journal of Machine Learning Research, 3:115-143, 2003.
4. Glorot, Xavier and Bengio, Yoshua. Understanding the difficulty of training deep feedforward neural networks. In International conference on artificial intelligence and statistics, pp. 249-256, 2010.
5. Goldberg, David E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989. ISBN 0201157675.
6. Gomez, Faustino, Schmidhuber, Jürgen, and Miikkulainen, Risto. Accelerated neural evolution through cooperatively coevolved synapses. The Journal of Machine Learning Research, 9:937-965, 2008.
7. Graves, Alex, Wayne, Greg, and Danihelka, Ivo. Neural turing machines. arXiv preprint arXiv. 1410.540l, 2014.
8. Grefenstette, Edward, Hermann, Karl Moritz, Suleyman, Mustafa, and Blunsom, Phil. Learning to transduce with unbounded memory. arXiv preprint arXiv:1506.02516, 2015.
9. Hasselt, Hado V. Double Q-learning. In Advances in Neural Information Processing Systems, pp. 2613-2621, 2010.
10. Hochreiter, Sepp and Schmidhuber, Jürgen. Long shortterm memory. Neural computation, 9(8):1735-1780, 1997.
11. Holland, John H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA, 1992. ISBN 0262082136.
12. Joulin, Armand and Mikolov, Tomas. Inferring algorithmic patterns with stack-augmented recurrent nets. arXiv preprint arXiv: 1503.01007, 2015.
13. Liang, Percy, Jordan, Michael I, and Klein, Dan. Learning dependency-based compositional semantics. Computational Linguistics, 39(2):389-446, 2013.
14. Loch, John and Singh, Satinder P. Using eligibility traces to find the best memoryless policy in partially observable markov decision processes. In ICML, pp. 323-331, 1998.
15. Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Graves, Alex, Antonoglou, Ioannis, Wierstra, Daan, and Riedmiller, Martin. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
16. Nordin, Peter. Evolutionary program induction of binary machine code and its applications. Krehl Munster, 1997.
17. Schmidhuber, Jürgen. Optimal ordered problem solver. Machine Learning, 54(3):211-254, 2004.
18. Solomonoff, Ray J. A formal theory of inductive inference. Part I. Information and control, 7(1):1-22, 1964.
19. Sukhbaatar, Sainbayar, Szlam, Arthur, Weston, Jason, and Fergus, Rob. Weakly supervised memory networks. arXiv preprint arXiv:1503.08895, 2015.
20. Sutton, Richard S and Barto, Andrew G. Reinforcement learning: An introduction, volume 1. MIT press Cambridge, 1998.
21. Watkins, Chris. Learning from Delayed Rewards. PhD thesis, Cambrdige University, 1989.
22. Williams, Ronald J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3):229256, 1992.
23. Williams, Ronald J and Zipser, David. Experimental analysis of the real-time recurrent learning algorithm. Connection Science, 1(1):87-111, 1989.
24. Wineberg, Mark and Oppacher, Franz. A representation scheme to perform program induction in a canonical genetic algorithm. In Parallel Problem Solving from NaturePPSNIII, pp. 291-301. Springer, 1994.
25. Zaremba, Wojciech and Sutskever, Ilya. Reinforcement learning neural turing machines. arXiv preprint arXiv: 1505.00527, 2015.