{"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/an-approximation-algorithm-for-optimal","title":"An Approximation Algorithm for Optimal Subarchitecture Extraction","arxiv_id":"2010.08512","date":"2020-10-16","proceeding":null,"authors":["Adrian de Wynter"],"abstract":"We consider the problem of finding the set of architectural parameters for a chosen deep neural network which is optimal under three metrics: parameter size, inference speed, and error rate. In this paper we state the problem formally, and present an approximation algorithm that, for a large subset of instances behaves like an FPTAS with an approximation error of $\\rho \\leq |{1- \\epsilon}|$, and that runs in $O(|{\\Xi}| + |{W^*_T}|(1 + |{\\Theta}||{B}||{\\Xi}|/({\\epsilon\\, s^{3/2})}))$ steps, where $\\epsilon$ and $s$ are input parameters; $|{B}|$ is the batch size; $|{W^*_T}|$ denotes the cardinality of the largest weight set assignment; and $|{\\Xi}|$ and $|{\\Theta}|$ are the cardinalities of the candidate architecture and hyperparameter spaces, respectively.","url_abs":"https://arxiv.org/abs/2010.08512v1","url_pdf":"https://arxiv.org/pdf/2010.08512v1.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":"an-approximation-algorithm-for-optimal","repo_url":"https://github.com/alexa/bort","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":null},{"paper_slug":"an-approximation-algorithm-for-optimal","repo_url":"https://github.com/kmz4/QHACK2021","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}