{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:26:35Z","timestamp":1790673995910,"version":"4.1.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Commun. ACM"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p>Proposing a community-based system for model development.<\/jats:p>","DOI":"10.1145\/3545111","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T15:29:38Z","timestamp":1674228578000},"page":"38-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Building Machine Learning Models Like Open Source Software"],"prefix":"10.1145","volume":"66","author":[{"given":"Colin","family":"Raffel","sequence":"first","affiliation":[{"name":"University of North Carolina, Chapel Hill Hill, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"et al. Estimating or propagating gradients through stochastic neurons for conditional computation.\" arXiv preprint arXiv:1308.3432","author":"Bengio Y.","year":"2013","unstructured":"Bengio, Y. et al. Estimating or propagating gradients through stochastic neurons for conditional computation.\" arXiv preprint arXiv:1308.3432 (2013)."},{"key":"e_1_2_1_2_1","volume-title":"International Conference on Learning Representations","author":"Chen T.","year":"2016","unstructured":"Chen, T. et al. Net2net: Accelerating learning via knowledge transfer. International Conference on Learning Representations (2016)."},{"key":"e_1_2_1_3_1","volume-title":"Machine learning is requirements engineering---On the role of bugs, verification, and validation in machine learning. Analytics Vidhya","author":"K\u00e4stner C.","year":"2022","unstructured":"K\u00e4stner, C. Machine learning is requirements engineering---On the role of bugs, verification, and validation in machine learning. Analytics Vidhya (2022)."},{"key":"e_1_2_1_4_1","volume-title":"et al. Federated learning: Strategies for improving communication efficiency. Private Multi-Party Machine Learning","author":"Konecn\u00fd J.","year":"2016","unstructured":"Konecn\u00fd, J. et al. Federated learning: Strategies for improving communication efficiency. Private Multi-Party Machine Learning (2016)."},{"key":"e_1_2_1_5_1","volume-title":"Merging models with Fisher-weighted averaging. arXiv preprint arXiv:2111.09832","author":"Matena M.","year":"2021","unstructured":"Matena, M. and Raffel, C. Merging models with Fisher-weighted averaging. arXiv preprint arXiv:2111.09832 (2021)."},{"key":"e_1_2_1_6_1","volume-title":"et al. Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics","author":"McMahan B.","year":"2017","unstructured":"McMahan, B. et al. Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics (2017)."},{"key":"e_1_2_1_7_1","volume-title":"et al. Training neural networks with fixed sparse masks. Advances in Neural Information Processing Systems","author":"Sung Y.-L.","year":"2021","unstructured":"Sung, Y.-L. et al. Training neural networks with fixed sparse masks. Advances in Neural Information Processing Systems (2021)."},{"key":"e_1_2_1_8_1","volume-title":"et al. Learning multiple visual domains with residual adapters. Advances in Neural Information Processing Systems","author":"Rebuffi S.-A.","year":"2017","unstructured":"Rebuffi, S.-A. et al. Learning multiple visual domains with residual adapters. Advances in Neural Information Processing Systems (2017)."},{"key":"e_1_2_1_9_1","volume-title":"et al. Multitask prompted training enables zero-shot task generalization.\" In Proceedings of the International Conference on Learning Representations","author":"Sanh V.","year":"2022","unstructured":"Sanh, V. et al. Multitask prompted training enables zero-shot task generalization.\" In Proceedings of the International Conference on Learning Representations (2022)."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Shazeer N.","year":"2017","unstructured":"Shazeer, N. et al. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In Proceedings of the International Conference on Learning Representations (2017)."}],"container-title":["Communications of the ACM"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545111","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3545111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:44Z","timestamp":1750186964000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,20]]},"references-count":10,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["10.1145\/3545111"],"URL":"https:\/\/doi.org\/10.1145\/3545111","relation":{},"ISSN":["0001-0782","1557-7317"],"issn-type":[{"value":"0001-0782","type":"print"},{"value":"1557-7317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,20]]},"assertion":[{"value":"2023-01-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}