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Program synthesis from input-output (IO) examples has been a long-standing challenge.
Karel the robot: a gentle introduction to the art of programming
R. E. Pattis · 1981
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Bootstrapping: A nonparametric approach to statistical inference
C. Z. Mooney, C. F. Mooney, C. L. Mooney, R. D. Duval, and R. Duvall · 1993
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Bootstrapping
S. Abney · 2002
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Effective self-training for parsing
D. McClosky, E. Charniak, and M. Johnson · 2006
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Automating string processing in spreadsheets using input-output examples
S. Gulwani · 2011
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Finding and understanding bugs in c compilers
X. Yang, Y. Chen, E. Eide, and J. Regehr · 2011
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An executable semantics for compcert c
B. Campbell · 2012
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Spreadsheet data manipulation using examples
S. Gulwani, W. R. Harris, and R. Singh · 2012
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W. Zaremba and I. Sutskever · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Effective approaches to attention-based neural machine translation
M.-T. Luong, H. Pham, and C. D. Manning · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deepcoder: Learning to write programs
M. Balog, A. L. Gaunt, M. Brockschmidt, S. Nowozin, and D. Tarlow · 2017
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Neural program meta-induction
J. Devlin, R. Bunel, R. Singh, M. Hausknecht, and P. Kohli · 2017
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Robustfill: Neural program learning under noisy I/O
J. Devlin, J. Uesato, S. Bhupatiraju, R. Singh, A. Mohamed, and P. Kohli · 2017
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Neuro-symbolic program synthesis
E. Parisotto, A.-r. Mohamed, R. Singh, L. Li, D. Zhou, and P. Kohli · 2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Leveraging grammar and reinforcement learning for neural program synthesis
R. Bunel, M. Hausknecht, J. Devlin, R. Singh, and P. Kohli · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Neural program search: Solving data processing tasks from description and examples, 2018
A. S. Illia Polosukhin · 2018
Cited alongside, same era.
Improving neural program synthesis with inferred execution traces
E. C. Shin, I. Polosukhin, and D. Song · 2018
Cited alongside, same era.
Neural program synthesis with a differentiable fixer
M. Balog, R. Singh, P. Maniatis, and C. Sutton · 2020
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Learning to execute programs with instruction pointer attention graph neural networks
D. Bieber, C. Sutton, H. Larochelle, and D. Tarlow · 2020
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Data generation for neural programming by example
J. Clymo, H. Manukian, N. Fijalkow, A. Gascón, and B. Paige · 2020
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Synthesize, execute and debug: Learning to repair for neural program synthesis
K. Gupta, P. E. Christensen, X. Chen, and D. Song · 2020
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Deep learning for symbolic mathematics
G. Lample and F. Charton · 2020
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Dialogue state induction using neural latent variable models
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Neural program synthesis from diverse demonstration videos
S.-H. Sun, H. Noh, S. Somasundaram, and J. Lim · 2018
Cited alongside, same era.
Neural-guided deductive search for real-time program synthesis from examples
A. J. Vijayakumar, A. Mohta, O. Polozov, D. Batra, P. Jain, and S. Gulwani · 2018
Cited alongside, same era.
Automatic program synthesis of long programs with a learned garbage collector
A. Zohar and L. Wolf · 2018
Cited alongside, same era.
A zero-positive learning approach for diagnosing software performance regressions
M. Alam, J. Gottschlich, N. Tatbul, J. S. Turek, T. Mattson, and A. Muzahid · 2019
Cited alongside, same era.
Execution-guided neural program synthesis
X. Chen, C. Liu, and D. Song · 2019
Cited alongside, same era.
Write, execute, assess: Program synthesis with a repl
K. Ellis, M. I. Nye, Y. Pu, F. Sosa, J. B. Tenenbaum, and A. Solar-Lezama · 2019
Cited alongside, same era.
Spoc: Search-based pseudocode to code
S. Kulal, P. Pasupat, K. Chandra, M. Lee, O. Padon, A. Aiken, and P. Liang · 2019
Cited alongside, same era.
Q. Min, L. Qin, Z. Teng, X. Liu, and Y. Zhang · 2020
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Learning to represent programs with property signatures
A. Odena and C. Sutton · 2020
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Creating synthetic datasets via evolution for neural program synthesis
A. Suh and Y. Timen · 2020
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Neural execution of graph algorithms
P. Veličković, R. Ying, M. Padovano, R. Hadsell, and C. Blundell · 2020
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Self-training with noisy student improves imagenet classification
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le · 2020
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Neural execution engines: Learning to execute subroutines
Y. Yan, K. Swersky, D. Koutra, P. Ranganathan, and M. Heshemi · 2020
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A probabilistic end-to-end task-oriented dialog model with latent belief states towards semi-supervised learning
Y. Zhang, Z. Ou, M. Hu, and J. Feng · 2020
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A large-scale benchmark for few-shot program induction and synthesis
F. Alet, J. Lopez-Contreras, J. Koppel, M. Nye, A. Solar-Lezama, T. Lozano-Perez, L. Kaelbling, and J. Tenenbaum · 2021
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Program synthesis with large language models
J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, et al · 2021
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Evaluating large language models trained on code
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. Ponde, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al · 2021
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Measuring coding challenge competence with apps
D. Hendrycks, S. Basart, S. Kadavath, M. Mazeika, A. Arora, E. Guo, C. Burns, S. Puranik, H. He, D. Song, et al · 2021
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Learning fitness functions for machine programming
S. Mandal, T. Anderson, J. Turek, J. Gottschlich, S. Zhou, and A. Muzahid · 2021
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Representing partial programs with blended abstract semantics
M. Nye, Y. Pu, M. Bowers, J. Andreas, J. B. Tenenbaum, and A. Solar-Lezama · 2021
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Bustle: Bottom-up program-synthesis through learning-guided exploration
A. Odena, K. Shi, D. Bieber, R. Singh, and C. Sutton · 2021
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