{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:18:05Z","timestamp":1778149085484,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":22,"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\/3394486.3406482","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:18:56Z","timestamp":1597965536000},"page":"3571-3572","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Deep Learning for Industrial AI"],"prefix":"10.1145","author":[{"given":"Chetan","family":"Gupta","sequence":"first","affiliation":[{"name":"Hitachi America Ltd., Santa Clara, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Farahat","sequence":"additional","affiliation":[{"name":"Hitachi America Ltd., Santa Clara, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622431"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the World Congress on Engineering and Computer Science","volume":"2","author":"Chandramouli Aravind","year":"2013","unstructured":"Aravind Chandramouli , Gopi Subramanian , Debasis Bal , SI Ao , C Douglas , WS Grundfest , and J Burgstone . 2013 . Unsupervised extraction of part names from service logs . In Proceedings of the World Congress on Engineering and Computer Science , Vol. 2 . Aravind Chandramouli, Gopi Subramanian, Debasis Bal, SI Ao, C Douglas, WS Grundfest, and J Burgstone. 2013. Unsupervised extraction of part names from service logs. In Proceedings of the World Congress on Engineering and Computer Science, Vol. 2."},{"key":"e_1_3_2_1_3_1","volume-title":"Automating Visual Inspection with Convolutional Neural Networks. In Annual Conference of the PHM Society","volume":"11","author":"Das Sreerupa","year":"2019","unstructured":"Sreerupa Das , Christopher D Hollander , and Suraiya Suliman . 2019 . Automating Visual Inspection with Convolutional Neural Networks. In Annual Conference of the PHM Society , Vol. 11 . Sreerupa Das, Christopher D Hollander, and Suraiya Suliman. 2019. Automating Visual Inspection with Convolutional Neural Networks. In Annual Conference of the PHM Society, Vol. 11."},{"key":"e_1_3_2_1_4_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems. 3844--3852.  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems. 3844--3852."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.36001\/phmconf.2018.v10i1.590"},{"key":"e_1_3_2_1_6_1","volume-title":"Reproducibility of benchmarked deep reinforcement learning tasks for continuous control. arXiv preprint arXiv:1708.04133","author":"Islam Riashat","year":"2017","unstructured":"Riashat Islam , Peter Henderson , Maziar Gomrokchi , and Doina Precup . 2017. Reproducibility of benchmarked deep reinforcement learning tasks for continuous control. arXiv preprint arXiv:1708.04133 ( 2017 ). Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup. 2017. Reproducibility of benchmarked deep reinforcement learning tasks for continuous control. arXiv preprint arXiv:1708.04133 (2017)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2019.8819403"},{"key":"e_1_3_2_1_8_1","volume-title":"1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management.","author":"Malhotra Pankaj","year":"2016","unstructured":"Pankaj Malhotra , Vishnu TV , Anusha Ramakrishnan , Gaurangi Anand , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2016 . Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder . In 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management. Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder. In 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1708283115"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2002.1007599"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM49022.2020.9187036"},{"key":"e_1_3_2_1_12_1","volume-title":"Cost-sensitive learning for predictive maintenance. arXiv preprint arXiv:1809.10979","author":"Spiegel Stephan","year":"2018","unstructured":"Stephan Spiegel , Fabian Mueller , Dorothea Weismann , and John Bird . 2018. Cost-sensitive learning for predictive maintenance. arXiv preprint arXiv:1809.10979 ( 2018 ). Stephan Spiegel, Fabian Mueller, Dorothea Weismann, and John Bird. 2018. Cost-sensitive learning for predictive maintenance. arXiv preprint arXiv:1809.10979 (2018)."},{"key":"e_1_3_2_1_13_1","volume-title":"Jure Skvarvc, and Danijel Skovc aj.","author":"Tabernik Domen","year":"2020","unstructured":"Domen Tabernik , Samo vS ela , Jure Skvarvc, and Danijel Skovc aj. 2020 . Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing , Vol. 31 , 3 (2020), 759--776. Domen Tabernik, Samo vS ela, Jure Skvarvc, and Danijel Skovc aj. 2020. Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, Vol. 31, 3 (2020), 759--776."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2019.8819420"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851700"},{"key":"e_1_3_2_1_16_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 488--504","author":"Zhang Chi","year":"2018","unstructured":"Chi Zhang , Chetan Gupta , Ahmed Farahat , Kosta Ristovski , and Dipanjan Ghosh . 2018 . Equipment health indicator learning using deep reinforcement learning . In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 488--504 . Chi Zhang, Chetan Gupta, Ahmed Farahat, Kosta Ristovski, and Dipanjan Ghosh. 2018. Equipment health indicator learning using deep reinforcement learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 488--504."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00030"},{"key":"e_1_3_2_1_18_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 621--637","author":"Zheng Shuai","year":"2019","unstructured":"Shuai Zheng , Ahmed Farahat , and Chetan Gupta . 2019 a. Generative adversarial networks for failure prediction . In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 621--637 . Shuai Zheng, Ahmed Farahat, and Chetan Gupta. 2019 a. Generative adversarial networks for failure prediction. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 621--637."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053475"},{"key":"e_1_3_2_1_20_1","volume-title":"ICASSP 2020--2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Zheng Shuai","unstructured":"Shuai Zheng and Chetan Gupta . 2020 b. Trace Norm Generative Adversarial Networks for Sensor Generation and Feature Extraction . In ICASSP 2020--2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE , 3187--3191. Shuai Zheng and Chetan Gupta. 2020 b. Trace Norm Generative Adversarial Networks for Sensor Generation and Feature Extraction. In ICASSP 2020--2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3187--3191."},{"key":"e_1_3_2_1_21_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 655--671","author":"Zheng Shuai","year":"2019","unstructured":"Shuai Zheng , Chetan Gupta , and Susumu Serita . 2019 b. Manufacturing Dispatching using Reinforcement and Transfer Learning . In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 655--671 . Shuai Zheng, Chetan Gupta, and Susumu Serita. 2019 b. Manufacturing Dispatching using Reinforcement and Transfer Learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 655--671."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2017.7998311"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"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\/3394486.3406482","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3406482","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\/3394486.3406482"}},"subtitle":["Challenges, New Methods and Best Practices"],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":22,"alternative-id":["10.1145\/3394486.3406482","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3406482","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"}}]}}