Work

Can Neural Networks Learn Symbolic Rewriting?

Year: 2019

Type: preprint

Abstract: This work investigates if the current neural architectures are adequate for learning symbolic rewriting. Two kinds of data sets are proposed for this research -- one based on automated proofs and the other being a synthetic set of polynomial terms. The experiments with use of the current neural machine translation models are performed and its results are discussed. Ideas for extending this line of research are proposed, and its relevance is motivated.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1911.04873

Authors: Piotrowski, Bartosz, Urban, Josef, Brown, Chad E., Kaliszyk, Cezary

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2989340591

Cites: 15

Cited by: 10

Related to: 10