{"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/automatic-differentiation-in-ml-where-we-are","title":"Automatic differentiation in ML: Where we are and where we should be going","arxiv_id":"1810.11530","date":"2018-10-26","proceeding":"NeurIPS 2018 12","authors":["Bart van Merriënboer","Olivier Breuleux","Arnaud Bergeron","Pascal Lamblin"],"abstract":"We review the current state of automatic differentiation (AD) for array\nprogramming in machine learning (ML), including the different approaches such\nas operator overloading (OO) and source transformation (ST) used for AD,\ngraph-based intermediate representations for programs, and source languages.\nBased on these insights, we introduce a new graph-based intermediate\nrepresentation (IR) which specifically aims to efficiently support\nfully-general AD for array programming. Unlike existing dataflow programming\nrepresentations in ML frameworks, our IR naturally supports function calls,\nhigher-order functions and recursion, making ML models easier to implement. The\nability to represent closures allows us to perform AD using ST without a tape,\nmaking the resulting derivative (adjoint) program amenable to ahead-of-time\noptimization using tools from functional language compilers, and enabling\nhigher-order derivatives. Lastly, we introduce a proof of concept compiler\ntoolchain called Myia which uses a subset of Python as a front end.","url_abs":"http://arxiv.org/abs/1810.11530v2","url_pdf":"http://arxiv.org/pdf/1810.11530v2.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":"automatic-differentiation-in-ml-where-we-are","repo_url":"https://github.com/mila-udem/myia","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.11530","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.11530"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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