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Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime interaction and more widespread adoption. Existing approaches to PBE synthesis have used automated reasoning tools, such as SMT solvers, as well as works applying machine learning techniques. At its core, the automated reasoning approach relies on highly domain specific knowledge of programming languages. On the other hand, the machine learning approaches utilize the fact that when working with program code, it is possible to generate arbitrarily large training datasets. In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural network, we can use a data driven approach to reduce the size of the search space, then allow automated reasoning-based solvers to more quickly find a solution analytically. Our system is able to run atop existing SyGuS PBE synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS Competition for the PBE Strings track by 47.65% to outperform all of the competing tools.
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Programming-by-example for audio: synthesizing digital signal processing programs
Programming by example allows users to create programs without coding, by simply specifying input and output pairs.We introduce the problem of digital signal processing programming by example (DSP-PBE), where users specify input and output wave files, and a tool automatically synthesizes a program that transforms the input to the output. This program can then be applied to new wave files, giving users a new way to interact with music and program code. We formally define the problem of DSP-PBE, and provide a first implementation of a solution that can handle synthesis over commutative filters.
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- PAR ID:
- 10113566
- Date Published:
- Journal Name:
- FARM 2018 Proceedings of the 6th ACM SIGPLAN International Workshop on Functional Art, Music, Modeling, and Design
- Page Range / eLocation ID:
- 18 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3242903.3242906
