7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Can Neural Networks Learn Symbolic Rewriting?

      Preprint

      , , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 7

          • Record: found
          • Abstract: not found
          • Book Chapter: not found

          Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            The TPTP Problem Library and Associated Infrastructure

              Bookmark
              • Record: found
              • Abstract: not found
              • Book Chapter: not found

              Loops with Abelian Inner Mapping Groups: An Application of Automated Deduction

                Bookmark

                Author and article information

                Journal
                07 November 2019
                Article
                1911.04873

                http://creativecommons.org/licenses/by/4.0/

                Custom metadata
                cs.AI cs.CL cs.LG

                Theoretical computer science, Artificial intelligence

                Comments

                Comment on this article