Abstract
We present a method for solving Programming by Example (PBE) problems that tightly integrates a neural network with a constraint logic programming system called miniKanren. Internally, miniKanren searches for a program that satisfies the recursive constraints imposed by the provided examples. Our Recurrent Neural Network (RNN) model uses these constraints as input to score candidate programs. We show evidence that using our method to guide miniKanren’s search is a promising approach to solving PBE problems.
| Original language | English |
|---|---|
| State | Published - 2018 |
| Externally published | Yes |
| Event | 6th International Conference on Learning Representations, ICLR 2018 - Vancouver, Canada Duration: 30 Apr 2018 → 3 May 2018 |
Conference
| Conference | 6th International Conference on Learning Representations, ICLR 2018 |
|---|---|
| Country/Territory | Canada |
| City | Vancouver |
| Period | 30/04/18 → 3/05/18 |
Bibliographical note
Publisher Copyright:© 6th International Conference on Learning Representations, ICLR 2018 - Workshop Track Proceedings. All rights reserved.
Funding
Research reported in this publication was supported in part by the Natural Sciences and Engineering Research Council of Canada, and the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number OT2TR002517. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
| Funders | Funder number |
|---|---|
| National Institutes of Health | OT2TR002517 |
| National Center for Advancing Translational Sciences | |
| Natural Sciences and Engineering Research Council of Canada |