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Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification.
Toward automatic program synthesis
Zohar Manna and Richard J Waldinger · 1971
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Prolog Programming for Artificial Intelligence
Ivan Bratko · 2001
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TF-Coder: Program synthesis for tensor manipulations
Kensen Shi, David Bieber, and Rishabh Singh · 2003
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Modeling semantic cognition as logical dimensionality reduction
Yarden Katz, Noah D Goodman, Kristian Kersting, Charles Kemp, and Joshua B Tenenbaum · 2008
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Search-based structured prediction
Hal Daumé III, John Langford, and Daniel Marcu · 2009
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Automating string processing in spreadsheets using input-output examples
Sumit Gulwani · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
Stephane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Solving mixed integer programs using neural networks
Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O’Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, and Yori Zwols · 2012
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A machine learning framework for programming by example
Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, and Adam Kalai · 2013
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Meta-interpretive learning of higher-order dyadic Datalog: Predicate invention revisited
Stephen Muggleton and Dianhuan Lin · 2013
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Stochastic superoptimization
Eric Schkufza, Rahul Sharma, and Alex Aiken · 2013
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TRANSIT: Specifying protocols with concolic snippets
Abhishek Udupa, Arun Raghavan, Jyotirmoy V Deshmukh, Sela Mador-Haim, Milo M K Martin, and Rajeev Alur · 2013
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Bias reformulation for one-shot function induction
Dianhuan Lin, Eyal Dechter, Kevin Ellis, Joshua B Tenenbaum, and Stephen H Muggleton · 2014
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Learning to search better than your teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, and John Langford · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Cited alongside, same era.
Thinking fast and slow with deep learning and tree search
Thomas Anthony, Zheng Tian, and David Barber · 2017
Cited alongside, same era.
DeepCoder: Learning to write programs
Matej Balog, Alexander L Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2017
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.
Program synthesis
Sumit Gulwani, Oleksandr Polozov, and Rishabh Singh · 2017
Cited alongside, same era.
Neuro-symbolic program synthesis
Automatic program synthesis of long programs with a learned garbage collector
Amit Zohar and Lior Wolf · 2018
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Execution-guided neural program synthesis
Xinyun Chen, Chang Liu, and Dawn Song · 2019
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Write, execute, assess: Program synthesis with a REPL
Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama · 2019
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Global relational models of source code
Vincent Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2019
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Learning to infer program sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, and Armando Solar-Lezama · 2019
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FrAngel: Component-based synthesis with control structures
Kensen Shi, Jacob Steinhardt, and Percy Liang · 2019
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Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2017
Cited alongside, same era.
A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton · 2018
Cited alongside, same era.
Leveraging grammar and reinforcement learning for neural program synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, and Pushmeet Kohli · 2018
Cited alongside, same era.
The three pillars of machine programming
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B Tenenbaum, and Tim Mattson · 2018
Cited alongside, same era.
Neural-guided deductive search for real-time program synthesis from examples
Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, and Sumit Gulwani · 2018
Cited alongside, same era.
Just-in-time learning for bottom-up enumerative synthesis
Shraddha Barke, Hila Peleg, and Nadia Polikarpova · 2020
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An empirical investigation of beam-aware training in supertagging
Renato Negrinho, Matthew Gormley, and Geoffrey Gordon · 2020
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Learning to represent programs with property signatures
Augustus Odena and Charles Sutton · 2020
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, Will Guss, Alex Nichol, Igor Babuschkin, Suchir Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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Turning 30: New ideas in inductive logic programming
Andrew Cropper, Sebastijan Dumančić, and Stephen H Muggleton · 2021
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Latent programmer: Discrete latent codes for program synthesis
Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, and Manzil Zaheer · 2021
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BUSTLE: Bottom-up program synthesis through learning-guided exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, and Hanjun Dai · 2021
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SmBoP: Semi-autoregressive bottom-up semantic parsing
Ohad Rubin and Jonathan Berant · 2021
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