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Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learning systems.
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Inductive programming meets the real world
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Flashmeta: a framework for inductive program synthesis
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Neural program search: Solving data processing tasks from description and examples
Polosukhin, I. and Skidanov, A · 2018
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Neural-symbolic vqa: Disentangling reasoning from vision and language understanding
Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., and Tenenbaum, J · 2018
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Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs
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A survey of reinforcement learning informed by natural language
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Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., and Wu, J · 2019
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Shaping visual representations with language for few-shot classification
Mu, J., Liang, P., and Goodman, N · 2019
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Learning to infer program sketches
Nye, M., Hewitt, L., Tenenbaum, J., and Solar-Lezama, A · 2019
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Program synthesis and semantic parsing with learned code idioms
Shin, E. C., Allamanis, M., Brockschmidt, M., and Polozov, A · 2019
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Learning a meta-solver for syntax-guided program synthesis
Si, X., Yang, Y., Dai, H., Naik, M., and Song, L · 2019
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Mutual exclusivity as a challenge for deep neural networks
Gandhi, K. and Lake, B. M · 2019
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Learning to infer program sketches
Nye, M., Hewitt, L., Tenenbaum, J., and Solar-Lezama, A · 2019
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Program synthesis and semantic parsing with learned code idioms
Shin, E. C., Allamanis, M., Brockschmidt, M., and Polozov, A · 2019
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Dreamcoder: Growing generalizable, interpretable knowledge with wake-sleep bayesian program learning
Ellis, K., Wong, C., Nye, M., Sablé-Meyer, M., Cary, L., Morales, L., Hewitt, L., Solar-Lezama, A., and Tenenbaum, J · 2020
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Pixl2r: Guiding reinforcement learning using natural language by mapping pixels to rewards
Goyal, P., Niekum, S., and Mooney, R. J · 2020
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Alice: Active learning with contrastive natural language explanations
Liang, W., Zou, J., and Yu, Z · 2020
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The child as hacker: building more human-like models of learning
Rule, J. S · 2020
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Few-shot bayesian imitation learning with logical program policies
Silver, T., Allen, K. R., Lew, A. K., Kaelbling, L. P., and Tenenbaum, J · 2020
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Learning abstract structure for drawing by efficient motor program induction
Tian, L. Y., Ellis, K., Kryven, M., and Tenenbaum, J. B · 2020
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Dreamcoder: Bootstrapping inductive programsynthesis with wake-sleep library learning
Ellis, K., Wong, C., Nye, M., Sablé-Meyer, M., Cary, L., Morales, L., Hewitt, L., Solar-Lezama, A., and Tenenbaum, J · 2021
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Macro grammars and holistic triggering for efficient semantic parsing
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Dreamcoder: Bootstrapping inductive programsynthesis with wake-sleep library learning
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