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LambdaBeam is a state-of-the-art, execution-guided algorithm for program synthesis that utilizes higher-order functions, lambda functions, and iterative loops within a Domain-Specific Language (DSL).
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Flashmeta: A framework for inductive program synthesis
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Yin, P.; and Neubig, G. 2017 · 2017
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Execution-guided neural program synthesis
Chen, X.; Liu, C.; and Song, D. 2018 · 2018
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Accelerating search-based program synthesis using learned probabilistic models
Lee, W.; Heo, K.; Alur, R.; and Naik, M. 2018 · 2018
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Automatic program synthesis of long programs with a learned garbage collector
Zohar, A.; and Wolf, L. 2018 · 2018
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Write, execute, assess: Program synthesis with a repl
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Learning to infer program sketches
Nye, M.; Hewitt, L.; Tenenbaum, J.; and Solar-Lezama, A. 2019 · 2019
Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning
Ellis, K.; Wong, C.; Nye, M.; Sablé-Meyer, M.; Morales, L.; Hewitt, L.; Cary, L.; Solar-Lezama, A.; and Tenenbaum, J. B. 2021 · 2021
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Latent programmer: Discrete latent codes for program synthesis
Hong, J.; Dohan, D.; Singh, R.; Sutton, C.; and Zaheer, M. 2021 · 2021
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Learning to combine per-example solutions for neural program synthesis
Shrivastava, D.; Larochelle, H.; and Tarlow, D. 2021 · 2021
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Tf-coder: Program synthesis for tensor manipulations
Shi, K.; Bieber, D.; and Singh, R. 2022 · 2022
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CrossBeam: Learning to search in bottom-up program synthesis
Shi, K.; Dai, H.; Ellis, K.; and Sutton, C. 2022 · 2022
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Frangel: component-based synthesis with control structures
Shi, K.; Steinhardt, J.; and Liang, P. 2019 · 2019
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Structural language models of code
Alon, U.; Sadaka, R.; Levy, O.; and Yahav, E. 2020 · 2020
Cited alongside, same era.
Just-in-time learning for bottom-up enumerative synthesis
Barke, S.; Peleg, H.; and Polikarpova, N. 2020 · 2020
Cited alongside, same era.
Incremental sampling without replacement for sequence models
Shi, K.; Bieber, D.; and Sutton, C. 2020 · 2020
Cited alongside, same era.
Deep learning with PyTorch
Stevens, E.; Antiga, L.; and Viehmann, T. 2020 · 2020
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Evaluating large language models trained on code
Chen, M.; Tworek, J.; Jun, H.; Yuan, Q.; Pinto, H. P. d. O.; Kaplan, J.; Edwards, H.; Burda, Y.; Joseph, N.; Brockman, G.; et al. 2021 · 2021
Cited alongside, same era.
Bowers, M.; Olausson, T. X.; Wong, L.; Grand, G.; Tenenbaum, J. B.; Ellis, K.; and Solar-Lezama, A. 2023 · 2023
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Lilo: Learning interpretable libraries by compressing and documenting code
Grand, G.; Wong, L.; Bowers, M.; Olausson, T. X.; Liu, M.; Tenenbaum, J. B.; and Andreas, J. 2023 · 2023
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Unraveling the arc puzzle: Mimicking human solutions with object-centric decision transformer
Park, J.; Im, J.; Hwang, S.; Lim, M.; Ualibekova, S.; Kim, S.; and Kim, S. 2023 · 2023
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Imitating human behaviour with diffusion models
Pearce, T.; Rashid, T.; Kanervisto, A.; Bignell, D.; Sun, M.; Georgescu, R.; Macua, S. V.; Tan, S. Z.; Momennejad, I.; Hofmann, K.; et al. 2023 · 2023
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Tan, J. C. M.; and Motani, M. 2023 · 2023
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Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
Zelikman, E.; Huang, Q.; Poesia, G.; Goodman, N.; and Haber, N. 2023 · 2023
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Hierarchical neural program synthesis
Zhong, L.; Lindeborg, R.; Zhang, J.; Lim, J. J.; and Sun, S.-H. 2023 · 2023
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LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
Shi, K.; Dai, H.; Li, W.-D.; Ellis, K.; and Sutton, C. 2024 · 2024
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