{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-continuous-semantic-representations","title":"Learning Continuous Semantic Representations of Symbolic Expressions","arxiv_id":"1611.01423","date":"2016-11-04","proceeding":"ICML 2017 8","authors":["Miltiadis Allamanis","Pankajan Chanthirasegaran","Pushmeet Kohli","Charles Sutton"],"abstract":"Combining abstract, symbolic reasoning with continuous neural reasoning is a\ngrand challenge of representation learning. As a step in this direction, we\npropose a new architecture, called neural equivalence networks, for the problem\nof learning continuous semantic representations of algebraic and logical\nexpressions. These networks are trained to represent semantic equivalence, even\nof expressions that are syntactically very different. The challenge is that\nsemantic representations must be computed in a syntax-directed manner, because\nsemantics is compositional, but at the same time, small changes in syntax can\nlead to very large changes in semantics, which can be difficult for continuous\nneural architectures. We perform an exhaustive evaluation on the task of\nchecking equivalence on a highly diverse class of symbolic algebraic and\nboolean expression types, showing that our model significantly outperforms\nexisting architectures.","url_abs":"http://arxiv.org/abs/1611.01423v2","url_pdf":"http://arxiv.org/pdf/1611.01423v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-continuous-semantic-representations","repo_url":"https://github.com/mast-group/eqnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.01423","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}