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Synthesizing programs using example input/outputs is a classic problem in artificial intelligence.
A methodology for lisp program construction from examples
Phillip D. Summers · 1977
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The inference of regular lisp programs from examples
Alan W. Biermann · 1978
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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From variadic functions to variadic relations
William E. Byrd and Daniel P. Friedman · 2006
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Automating string processing in spreadsheets using input-output examples
Sumit Gulwani · 2011
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Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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miniKanren, live and untagged: Quine generation via relational interpreters (programming pearl)
William E. Byrd, Eric Holk, and Daniel P. Friedman · 2012
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μ \mu kanren: A minimal functional core for relational programming
Jason Hemann and Daniel P. Friedman · 2013
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Recursive program synthesis
Aws Albarghouthi, Sumit Gulwani, and Zachary Kincaid · 2013
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Synthesizing data structure transformations from input-output examples
John K. Feser, Swarat Chaudhuri, and Isil Dillig · 2015
Cited alongside, same era.
Type-and-example-directed program synthesis
Peter-Michael Osera and Steve Zdancewic · 2015
Cited alongside, same era.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
Cited alongside, same era.
Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V. Le, and Ilya Sutskever · 2016
Cited alongside, same era.
Neural programmer-interpreters
Scott Reed and Nando de Freitas · 2016
Cited alongside, same era.
Terpret: A probabilistic programming language for program induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, and Daniel Tarlow · 2016
Neuro-symbolic program synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2017
Later among the works it cites.
Deepcoder: Learning to write programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2017
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A unified approach to solving seven programming problems (functional pearl)
William E. Byrd, Michael Ballantyne, Gregory Rosenblatt, and Matthew Might · 2017
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Deep API programmer: Learning to program with APIs
Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
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End-to-end differentiable proving
Tim Rocktäschel and Sebastian Riedel · 2017
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Neural-guided deductive search for real-time program synthesis from examples
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Cited alongside, same era.
Sampling for bayesian program learning
Kevin Ellis, Armando Solar-Lezama, and Josh Tenenbaum · 2016
Cited alongside, same era.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
Cited alongside, same era.
Prioritized experience replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
Cited alongside, same era.
RobustFill: Neural program learning under noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
Cited alongside, same era.
Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, and Sumit Gulwani · 2018
Closest in time.
Towards synthesizing complex programs from input-output examples
Xinyun Chen, Chang Liu, and Dawn Song · 2018
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2018
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Tree-to-tree neural networks for program translation
Xinyun Chen, Chang Liu, and Dawn Song · 2018
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Learning a sat solver from single-bit supervision
Daniel Selsam, Matthew Lamm, Benedikt Bunz, Percy Liang, Leonardo de Moura, and David L Dill · 2018
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