{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:06:55Z","timestamp":1775912815360,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong Theme-based Research Scheme","award":["TRS grant T41-709\/17N"],"award-info":[{"award-number":["TRS grant T41-709\/17N"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330908","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"903-913","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":87,"title":["Interpretable and Steerable Sequence Learning via Prototypes"],"prefix":"10.1145","author":[{"given":"Yao","family":"Ming","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Panpan","family":"Xu","sequence":"additional","affiliation":[{"name":"Bosch Research North America, Sunnyvale, CA, USA"}]},{"given":"Huamin","family":"Qu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Liu","family":"Ren","sequence":"additional","affiliation":[{"name":"Bosch Research North America, Sunnyvale, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1068"},{"key":"e_1_3_2_1_2_1","volume-title":"Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms . Standard","author":"AAMI.","unstructured":"ANSI\/ AAMI. 2008. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms . Standard . American National Standards Institute, Inc. ( ANSI ), Association for the Advancement of Medical Instrumentation (AAMI). ANSI\/AAMI. 2008. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms . Standard. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939761"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788613"},{"key":"e_1_3_2_1_5_1","volume-title":"AMIA Annual Symposium Proceedings","volume":"2016","author":"Che Zhengping","year":"2016","unstructured":"Zhengping Che , Sanjay Purushotham , Robinder Khemani , and Yan Liu . 2016 . Interpretable deep models for icu outcome prediction . In AMIA Annual Symposium Proceedings , Vol. 2016 . American Medical Informatics Association, 371. Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu. 2016. Interpretable deep models for icu outcome prediction. In AMIA Annual Symposium Proceedings, Vol. 2016. American Medical Informatics Association, 371."},{"key":"e_1_3_2_1_6_1","volume-title":"This looks like that: deep learning for interpretable image recognition. arXiv preprint arXiv:1806.10574","author":"Chen Chaofan","year":"2018","unstructured":"Chaofan Chen , Oscar Li , Alina Barnett , Jonathan Su , and Cynthia Rudin . 2018a. This looks like that: deep learning for interpretable image recognition. arXiv preprint arXiv:1806.10574 ( 2018 ). Chaofan Chen, Oscar Li, Alina Barnett, Jonathan Su, and Cynthia Rudin. 2018a. This looks like that: deep learning for interpretable image recognition. arXiv preprint arXiv:1806.10574 (2018)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2745083"},{"key":"e_1_3_2_1_8_1","volume-title":"Machine Learning for Healthcare Conference. 301--318","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Andy Schuetz , Walter F Stewart , and Jimeng Sun . 2016 a. Doctor ai: Predicting clinical events via recurrent neural networks . In Machine Learning for Healthcare Conference. 301--318 . Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2016a. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference. 301--318."},{"key":"e_1_3_2_1_9_1","volume-title":"Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart.","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016 b. Retain : An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems . 3504--3512. Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016b. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems. 3504--3512."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw112"},{"key":"e_1_3_2_1_11_1","volume-title":"Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608","author":"Doshi-Velez Finale","year":"2017","unstructured":"Finale Doshi-Velez and Been Kim . 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 ( 2017 ). Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)."},{"key":"e_1_3_2_1_12_1","volume-title":"Int. Conf. Acoustics, Speech and Signal Processing. IEEE, 6645--6649","author":"Graves Alex","unstructured":"Alex Graves , Abdel-rahman Mohamed, and Geoffrey E. Hinton . 2013. Speech recognition with deep recurrent neural networks . In Int. Conf. Acoustics, Speech and Signal Processing. IEEE, 6645--6649 . Alex Graves, Abdel-rahman Mohamed, and Geoffrey E. Hinton. 2013. Speech recognition with deep recurrent neural networks. In Int. Conf. Acoustics, Speech and Signal Processing. IEEE, 6645--6649."},{"key":"e_1_3_2_1_13_1","volume-title":"Multitask learning and benchmarking with clinical time series data. arXiv preprint arXiv:1703.07771","author":"Harutyunyan Hrayr","year":"2017","unstructured":"Hrayr Harutyunyan , Hrant Khachatrian , David C Kale , and Aram Galstyan . 2017. Multitask learning and benchmarking with clinical time series data. arXiv preprint arXiv:1703.07771 ( 2017 ). Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, and Aram Galstyan. 2017. Multitask learning and benchmarking with clinical time series data. arXiv preprint arXiv:1703.07771 (2017)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_15_1","volume-title":"Leo Anthony Celi, and Roger G Mark","author":"Johnson Alistair EW","year":"2016","unstructured":"Alistair EW Johnson , Tom J Pollard , Lu Shen , H Lehman Li-wei, Mengling Feng , Mohammad Ghassemi , Benjamin Moody , Peter Szolovits , Leo Anthony Celi, and Roger G Mark . 2016 . MIMIC-III, a freely accessible critical care database. Scientific data , Vol. 3 (2016), 160035. Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific data , Vol. 3 (2016), 160035."},{"key":"e_1_3_2_1_16_1","volume-title":"ECG Heartbeat Classification: A Deep Transferable Representation. arXiv preprint arXiv:1805.00794","author":"Kachuee Mohammad","year":"2018","unstructured":"Mohammad Kachuee , Shayan Fazeli , and Majid Sarrafzadeh . 2018. ECG Heartbeat Classification: A Deep Transferable Representation. arXiv preprint arXiv:1805.00794 ( 2018 ). Mohammad Kachuee, Shayan Fazeli, and Majid Sarrafzadeh. 2018. ECG Heartbeat Classification: A Deep Transferable Representation. arXiv preprint arXiv:1805.00794 (2018)."},{"key":"e_1_3_2_1_17_1","volume-title":"Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078","author":"Karpathy Andrej","year":"2015","unstructured":"Andrej Karpathy , Justin Johnson , and Li Fei-Fei . 2015. Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078 ( 2015 ). Andrej Karpathy, Justin Johnson, and Li Fei-Fei. 2015. Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078 (2015)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00155578"},{"key":"e_1_3_2_1_19_1","volume-title":"AAAI Conference on Artificial Intelligence .","author":"Li Oscar","year":"2018","unstructured":"Oscar Li , Hao Liu , Chaofan Chen , and Cynthia Rudin . 2018 . Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions . In AAAI Conference on Artificial Intelligence . Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin. 2018. Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions. In AAAI Conference on Artificial Intelligence ."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3233231"},{"key":"e_1_3_2_1_21_1","volume-title":"A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019","author":"Lipton Zachary C","year":"2015","unstructured":"Zachary C Lipton , John Berkowitz , and Charles Elkan . 2015. A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 ( 2015 ). Zachary C Lipton, John Berkowitz, and Charles Elkan. 2015. A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)."},{"key":"e_1_3_2_1_22_1","volume-title":"International Conference on Learning Representations .","author":"Murdoch W. James","year":"2018","unstructured":"W. James Murdoch , Peter J. Liu , and Bin Yu . 2018 . Beyond Word Importance: Contextual Decomposition to Extract Interactions from LS\u2122s . In International Conference on Learning Representations . W. James Murdoch, Peter J. Liu, and Bin Yu. 2018. Beyond Word Importance: Contextual Decomposition to Extract Interactions from LS\u2122s. In International Conference on Learning Representations ."},{"key":"e_1_3_2_1_23_1","unstructured":"W James Murdoch and Arthur Szlam. 2017. Automatic Rule Extraction from Long Short Term Memory Networks. (2017).  W James Murdoch and Arthur Szlam. 2017. Automatic Rule Extraction from Long Short Term Memory Networks. (2017)."},{"key":"e_1_3_2_1_24_1","unstructured":"Parliament and Council of the European Union. 2016. The General Data Protection Regulation. (2016).  Parliament and Council of the European Union. 2016. The General Data Protection Regulation. (2016)."},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the 30th International Conference on International Conference on Machine Learning -","volume":"28","author":"Pascanu Razvan","year":"2013","unstructured":"Razvan Pascanu , Tomas Mikolov , and Yoshua Bengio . 2013 . On the Difficulty of Training Recurrent Neural Networks . In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML'13). 1310--1318. Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the Difficulty of Training Recurrent Neural Networks. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML'13). 1310--1318."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"e_1_3_2_1_28_1","volume-title":"Artificial intelligence","author":"Rich Elaine","unstructured":"Elaine Rich and Kevin Knight . 1991. Artificial intelligence . Tata McGraw-Hill . Elaine Rich and Kevin Knight. 1991. Artificial intelligence .Tata McGraw-Hill."},{"key":"e_1_3_2_1_29_1","volume-title":"Please Stop Explaining Black Box Models for High Stakes Decisions. NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning","author":"Rudin Cynthia","year":"2018","unstructured":"Cynthia Rudin . 2018 . Please Stop Explaining Black Box Models for High Stakes Decisions. NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning (2018). Cynthia Rudin. 2018. Please Stop Explaining Black Box Models for High Stakes Decisions. NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning (2018)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1386-5056(01)00221-0"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744158"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2408736.2408740"},{"key":"e_1_3_2_1_33_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V . Le . 2014 . Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems (NIPS '14). 3104--3112. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems (NIPS'14). 3104--3112."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1167"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","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 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330908","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330908","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:03Z","timestamp":1750206363000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":34,"alternative-id":["10.1145\/3292500.3330908","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330908","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}