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DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning
Open accessAbstract
We present a system for inductive program synthesis called DreamCoder, which inputs a corpus of synthesis problems each specified by one or a few examples, and automatically derives a library of program components and a neural search policy that can be used to efficiently solve other similar synthesis problems. The library and search policy bootstrap each other iteratively through a variant of "wake-sleep" approximate Bayesian learning. A new refactoring algorithm based on E-graph matching identifies common sub-components across synthesized programs, building a progressively deepening library of abstractions capturing the structure of the input domain. We evaluate on eight domains including classic program synthesis areas and AI tasks such as planning, inverse graphics, and equation discovery. We show that jointly learning the library and neural search policy leads to solving more problems, and solving them more quickly.
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“Indeed, recently an external generative model that created “dream-like” input helped train a DNN to produce the code behind a given mathematical mapping.148”
From The overfitted brain: Dreams evolved to assist generalization (2021) · cites this paper as [148]
In the citing paper
“The evidence for this possibility comes from common methods in deep learning that improve generalization, such as dropout,126 domain randomization,144 and the use of input data created by stochastic stimulation of generative models,148 which together bear striking similarities to the properties of dreams.”
From The overfitted brain: Dreams evolved to assist generalization (2021) · cites this paper as [148]
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Metadata
- Authors
- Ellis, K., Wong, C., Nye, M., Sablé-Meyer, M., Morales, L., Hewitt, L., Cary, L., Solar-Lezama, A., & Tenenbaum, J. B.
- Affiliations
- Cornell University (US)
- Massachusetts Institute of Technology (US)
- Collège de France (FR)
- ORCID
- Venue
- Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
- Publisher
- ACM
- Published
- 2021-06-18
- Pages
- 835-850
- ISSN
- 1531-7102 (ISSN-L)
- OpenAlex
- W3174357804
- Cited by
- 103
- References
- 56
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 18 · 2023: 30 · 2022: 10 · 2021: 7
- FWCI
- 7.51
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation (1918839 and 1231216)
- Air Force Office of Scientific Research (FA9550-16-1-0012)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- inductive program synthesis, bootstrapping, refactoring, program abstraction, planning, inverse graphics, equation discovery
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