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We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs.
Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1
Rumelhart, D. E., Hinton, G. E., and McClelland, J. L · 1986
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Catastrophic interference in connectionist networks: The sequential learning problem
Mccloskey, Michael and Cohen, Neal J · 1989
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Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Schmidhuber, Jürgen · 1992
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Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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Genetic programming: An introduction , volume 1
Banzhaf, Wolfgang, Nordin, Peter, Keller, Robert E, and Francone, Frank D · 1998
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, Richard S., Precup, Doina, and Singh, Satinder · 1999
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Hierarchical reinforcement learning with the MAXQ value function decomposition
Dietterich, Thomas G · 2000
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Programmable reinforcement learning agents
Andre, David and Russell, Stuart J · 2001
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Controlled and automatic processing: behavior, theory, and biological mechanisms
Schneider, Walter and Chein, Jason M · 2003
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Hierarchical apprenticeship learning with application to quadruped locomotion
Kolter, Zico, Abbeel, Pieter, and Ng, Andrew Y · 2008
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Using matrices to model symbolic relationship
Sutskever, Ilya and Hinton, Geoffrey E · 2009
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Neural reuse: A fundamental organizational principle of the brain
Anderson, Michael L · 2010
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Learning options through human interaction
Subramanian, Kaushik, Isbell, Charles, and Thomaz, Andrea · 2011
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Programming in the brain: A neural network theoretical framework
Donnarumma, Francesco, Prevete, Roberto, and Trautteur, Giuseppe · 2012
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3D object detection and viewpoint estimation with a deformable 3D cuboid model
Fidler, Sanja, Dickinson, Sven, and Urtasun, Raquel · 2012
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Modular inverse reinforcement learning for visuomotor behavior
Rothkopf, ConstantinA. and Ballard, DanaH · 2013
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Graves, Alex, Wayne, Greg, and Danihelka, Ivo · 2014
A programmer–interpreter neural network architecture for prefrontal cognitive control
Donnarumma, Francesco, Prevete, Roberto, Chersi, Fabian, and Pezzulo, Giovanni · 2015
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Inferring algorithmic patterns with stack-augmented recurrent nets
Joulin, Armand and Mikolov, Tomas · 2015
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Kaiser, Łukasz and Sutskever, Ilya · 2015
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2015
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Kurach, Karol, Andrychowicz, Marcin, and Sutskever, Ilya · 2015
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Neural programmer: Inducing latent programs with gradient descent
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Building program vector representations for deep learning
Mou, Lili, Li, Ge, Liu, Yuxuan, Peng, Hao, Jin, Zhi, Xu, Yan, and Zhang, Lu · 2014
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Complementary learning systems
O’Reilly, Randall C., Bhattacharyya, Rajan, Howard, Michael D., and Ketz, Nicholas · 2014
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Sequence to sequence learning with neural networks
Sutskever, Ilya, Vinyals, Oriol, and Le, Quoc VV · 2014
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Zaremba, Wojciech and Sutskever, Ilya · 2014
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Universal value function approximators
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Pointer networks
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Learning simple algorithms from examples
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