{"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/adaptive-neural-compilation","title":"Adaptive Neural Compilation","arxiv_id":"1605.07969","date":"2016-05-25","proceeding":"NeurIPS 2016 12","authors":["Rudy Bunel","Alban Desmaison","Pushmeet Kohli","Philip H. S. Torr","M. Pawan Kumar"],"abstract":"This paper proposes an adaptive neural-compilation framework to address the\nproblem of efficient program learning. Traditional code optimisation strategies\nused in compilers are based on applying pre-specified set of transformations\nthat make the code faster to execute without changing its semantics. In\ncontrast, our work involves adapting programs to make them more efficient while\nconsidering correctness only on a target input distribution. Our approach is\ninspired by the recent works on differentiable representations of programs. We\nshow that it is possible to compile programs written in a low-level language to\na differentiable representation. We also show how programs in this\nrepresentation can be optimised to make them efficient on a target distribution\nof inputs. Experimental results demonstrate that our approach enables learning\nspecifically-tuned algorithms for given data distributions with a high success\nrate.","url_abs":"http://arxiv.org/abs/1605.07969v2","url_pdf":"http://arxiv.org/pdf/1605.07969v2.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":"adaptive-neural-compilation","repo_url":"https://github.com/albanD/adaptive-neural-compilation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}