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Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs

Arabshahi, Forough and Singh, Sameer and Anandkumar, Animashree (2018) Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs. In: 6th International Conference on Learning Representations (ICLR 2018), 30 April-3 May 2018, Vancouver, Canada. https://resolver.caltech.edu/CaltechAUTHORS:20190327-085732435

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Abstract

Neural programming involves training neural networks to learn programs, mathematics, or logic from data. Previous works have failed to achieve good generalization performance, especially on problems and programs with high complexity or on large domains. This is because they mostly rely either on black-box function evaluations that do not capture the structure of the program, or on detailed execution traces that are expensive to obtain, and hence the training data has poor coverage of the domain under consideration. We present a novel framework that utilizes black-box function evaluations, in conjunction with symbolic expressions that define relationships between the given functions. We employ tree LSTMs to incorporate the structure of the symbolic expression trees. We use tree encoding for numbers present in function evaluation data, based on their decimal representation. We present an evaluation benchmark for this task to demonstrate our proposed model combines symbolic reasoning and function evaluation in a fruitful manner, obtaining high accuracies in our experiments. Our framework generalizes significantly better to expressions of higher depth and is able to fill partial equations with valid completions.


Item Type:Conference or Workshop Item (Poster)
Related URLs:
URLURL TypeDescription
https://openreview.net/forum?id=Hksj2WWAWPublisherArticle
http://arxiv.org/abs/1801.04342arXivArticle
Additional Information:The authors would like to thank Amazon Inc., for the AWS credits. F. Arabshahi is supported by DARPA Award D17AP00002. A. Anandkumar is supported by Microsoft Faculty Fellowship, NSF CAREER Award CCF-1254106, DARPA Award D17AP00002 and Air Force Award FA9550-15-1-0221. S. Singh would like to thank Adobe Research and FICO for supporting this research
Funders:
Funding AgencyGrant Number
Amazon Web ServicesUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)D17AP00002
Microsoft Faculty FellowshipUNSPECIFIED
NSFCCF-1254106
Air Force Office of Scientific Research (AFOSR)FA9550-15-1-0221
AdobeUNSPECIFIED
FICOUNSPECIFIED
Subject Keywords:symbolic reasoning, mathematical equations, recursive neural networks, neural programing
Record Number:CaltechAUTHORS:20190327-085732435
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190327-085732435
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94169
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:29 Mar 2019 20:09
Last Modified:03 Oct 2019 21:01

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