{"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/recent-advances-in-neural-program-synthesis","title":"Recent Advances in Neural Program Synthesis","arxiv_id":"1802.02353","date":"2018-02-07","proceeding":null,"authors":["Neel Kant"],"abstract":"In recent years, deep learning has made tremendous progress in a number of\nfields that were previously out of reach for artificial intelligence. The\nsuccesses in these problems has led researchers to consider the possibilities\nfor intelligent systems to tackle a problem that humans have only recently\nthemselves considered: program synthesis. This challenge is unlike others such\nas object recognition and speech translation, since its abstract nature and\ndemand for rigor make it difficult even for human minds to attempt. While it is\nstill far from being solved or even competitive with most existing methods,\nneural program synthesis is a rapidly growing discipline which holds great\npromise if completely realized. In this paper, we start with exploring the\nproblem statement and challenges of program synthesis. Then, we examine the\nfascinating evolution of program induction models, along with how they have\nsucceeded, failed and been reimagined since. Finally, we conclude with a\ncontrastive look at program synthesis and future research recommendations for\nthe field.","url_abs":"http://arxiv.org/abs/1802.02353v1","url_pdf":"http://arxiv.org/pdf/1802.02353v1.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":"recent-advances-in-neural-program-synthesis","repo_url":"https://github.com/qu-arx/arx-inf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"program-synthesis","task_name":"Program Synthesis"},{"task_slug":"program-induction","task_name":"Program induction"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.02353","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}