{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T20:57:09Z","timestamp":1764277029949,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3406701","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:03:55Z","timestamp":1597964635000},"page":"3501-3502","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Neural Structured Learning"],"prefix":"10.1145","author":[{"given":"Arjun","family":"Gopalan","sequence":"first","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da-Cheng","family":"Juan","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cesar Ilharco","family":"Magalhaes","sequence":"additional","affiliation":[{"name":"Google Research, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Sung","family":"Ferng","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Allan","family":"Heydon","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Ta","family":"Lu","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Pham","sequence":"additional","affiliation":[{"name":"Google Research, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Yu","sequence":"additional","affiliation":[{"name":"Google Research, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2018. Graph Nets. https:\/\/github.com\/deepmind\/graph_nets  2018. Graph Nets. https:\/\/github.com\/deepmind\/graph_nets"},{"key":"e_1_3_2_1_2_1","unstructured":"2019. Neural Structured Learning in TensorFlow. https:\/\/www.tensorflow.org\/ neural_structured_learning  2019. Neural Structured Learning in TensorFlow. https:\/\/www.tensorflow.org\/ neural_structured_learning"},{"key":"e_1_3_2_1_3_1","volume-title":"Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265--283.","author":"Abadi Mart\u00edin","year":"2016","unstructured":"Mart\u00edin Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg S. Corrado , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Ian Goodfellow , Andrew Harp , Geoffrey Irving , Michael Isard , Yangqing Jia , Rafal Jozefowicz , Lukasz Kaiser , Manjunath Kudlur , Josh Levenberg , Dandelion Man\u00e9 , Rajat Monga , Sherry Moore , Derek Murray , Chris Olah , Mike Schuster , Jonathon Shlens , Benoit Steiner , Ilya Sutskever , Kunal Talwar , Paul Tucker , Vincent Vanhoucke , Vijay Vasudevan , Fernanda Vi\u00e9gas , Oriol Vinyals , Pete Warden , Martin Wattenberg , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . 2016 . Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265--283. Mart\u00edin Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Man\u00e9, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Vi\u00e9gas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265--283."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1431"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098021"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159731"},{"key":"e_1_3_2_1_7_1","volume-title":"Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations.","author":"Goodfellow Ian","year":"2015","unstructured":"Ian Goodfellow , Jonathon Shlens , and Christian Szegedy . 2015 . Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371784"},{"key":"e_1_3_2_1_9_1","volume-title":"Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning","author":"Miyato Takeru","year":"2019","unstructured":"Takeru Miyato , Shin-Ichi Maeda , Masanori Koyama , and Shin Ishii . 2019. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2019 ), 1979--1993. Takeru Miyato, Shin-Ichi Maeda, Masanori Koyama, and Shin Ishii. 2019. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (2019), 1979--1993."},{"volume-title":"Advances in Neural Information Processing Systems 32. Curran Associates","author":"Stretcu Otilia","key":"e_1_3_2_1_10_1","unstructured":"Otilia Stretcu , Krishnamurthy Viswanathan , Dana Movshovitz-Attias , Emmanouil Platanios , Sujith Ravi , and Andrew Tomkins . 2019. Graph Agreement Models for Semi-Supervised Learning . In Advances in Neural Information Processing Systems 32. Curran Associates , Inc ., 8713--8723. Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Sujith Ravi, and Andrew Tomkins. 2019. Graph Agreement Models for Semi-Supervised Learning. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 8713--8723."},{"key":"e_1_3_2_1_11_1","volume-title":"Yu","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu , Shirui Pan , Fengwen Chen , Guodong Long , Chengqi Zhang , and Philip S . Yu . 2020 . A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems ( 2020), 1--21. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2020. A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems (2020), 1--21."}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Virtual Event CA USA","acronym":"KDD '20"},"container-title":["Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3406701","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3406701","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:30Z","timestamp":1750195890000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3406701"}},"subtitle":["Training Neural Networks with Structured Signals"],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":11,"alternative-id":["10.1145\/3394486.3406701","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3406701","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}