{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:37:48Z","timestamp":1765280268231,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singapore Ministry of Education Academic Research Fund Tier 3","award":["MOE2017-T3-1-007"],"award-info":[{"award-number":["MOE2017-T3-1-007"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,17]]},"DOI":"10.1145\/3474085.3475176","type":"proceedings-article","created":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T20:00:05Z","timestamp":1634587205000},"page":"1293-1302","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis"],"prefix":"10.1145","author":[{"given":"Naili","family":"Xing","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Sai Ho","family":"Yeung","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Cheng-Hao","family":"Cai","sequence":"additional","affiliation":[{"name":"National University of Singapore &amp; National University of Singapore (Suzhou) Research Institute, Singapore, Singapore"}]},{"given":"Teck Khim","family":"Ng","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Kaiyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Nan","family":"Yang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]},{"given":"Meihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Beijing, China"}]},{"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2021,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3026877.3026899"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045410"},{"key":"e_1_3_2_2_3_1","volume-title":"Large-Scale Machine Learning with Stochastic Gradient Descent. In 19th International Conference on Computational Statistics (COMPSTAT). Springer","author":"Bottou L\u00e9","year":"2010","unstructured":"L\u00e9 on Bottou . 2010 . Large-Scale Machine Learning with Stochastic Gradient Descent. In 19th International Conference on Computational Statistics (COMPSTAT). Springer , Paris, France, 177--186. L\u00e9 on Bottou. 2010. Large-Scale Machine Learning with Stochastic Gradient Descent. In 19th International Conference on Computational Statistics (COMPSTAT). Springer, Paris, France, 177--186."},{"volume-title":"Neural Networks: Tricks of the Trade -","author":"Bottou L\u00e9","key":"e_1_3_2_2_4_1","unstructured":"L\u00e9 on Bottou . 2012. Stochastic Gradient Descent Tricks . In Neural Networks: Tricks of the Trade - Second Edition. Springer , Heidelberg, Berlin , 421--436. L\u00e9 on Bottou. 2012. Stochastic Gradient Descent Tricks. In Neural Networks: Tricks of the Trade - Second Edition. Springer, Heidelberg, Berlin, 421--436."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3364324.3364325"},{"key":"e_1_3_2_2_6_1","volume-title":"2019 b. Effective and Efficient Dropout for Deep Convolutional Neural Networks. CoRR","author":"Cai Shaofeng","year":"2019","unstructured":"Shaofeng Cai , Jinyang Gao , Meihui Zhang , Wei Wang , Gang Chen , and Beng Chin Ooi . 2019 b. Effective and Efficient Dropout for Deep Convolutional Neural Networks. CoRR , Vol. abs\/ 1904 .03392 ( 2019 ). arxiv: 1904.03392 http:\/\/arxiv.org\/abs\/1904.03392 Shaofeng Cai, Jinyang Gao, Meihui Zhang, Wei Wang, Gang Chen, and Beng Chin Ooi. 2019 b. Effective and Efficient Dropout for Deep Convolutional Neural Networks. CoRR, Vol. abs\/1904.03392 (2019). arxiv: 1904.03392 http:\/\/arxiv.org\/abs\/1904.03392"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3154630.3154681"},{"key":"#cr-split#-e_1_3_2_2_9_1.1","doi-asserted-by":"crossref","unstructured":"Jia Deng Wei Dong Richard Socher Li-Jia Li Kai Li and Fei-Fei Li. 2009. ImageNet: A large-scale hierarchical image database. (2009) 248--255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848 10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"#cr-split#-e_1_3_2_2_9_1.2","doi-asserted-by":"crossref","unstructured":"Jia Deng Wei Dong Richard Socher Li-Jia Li Kai Li and Fei-Fei Li. 2009. ImageNet: A large-scale hierarchical image database. (2009) 248--255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_2_10_1","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). NAACL","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). NAACL , Minneapolis, MN, USA, 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). NAACL, Minneapolis, MN, USA, 4171--4186."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969442.2969547"},{"key":"e_1_3_2_2_12_1","volume-title":"Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomedical engineering online","author":"Foster Kenneth R","year":"2014","unstructured":"Kenneth R Foster , Robert Koprowski , and Joseph D Skufca . 2014. Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomedical engineering online , Vol. 13 ( 2014 ), 94. Kenneth R Foster, Robert Koprowski, and Joseph D Skufca. 2014. Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomedical engineering online, Vol. 13 (2014), 94."},{"key":"e_1_3_2_2_13_1","volume-title":"AllenNLP: A Deep Semantic Natural Language Processing Platform. CoRR","author":"Gardner Matt","year":"2018","unstructured":"Matt Gardner , Joel Grus , Mark Neumann , Oyvind Tafjord , Pradeep Dasigi , Nelson F. Liu , Matthew E. Peters , Michael Schmitz , and Luke Zettlemoyer . 2018. AllenNLP: A Deep Semantic Natural Language Processing Platform. CoRR , Vol. abs\/ 1803 .07640 ( 2018 ), 6 pages. Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew E. Peters, Michael Schmitz, and Luke Zettlemoyer. 2018. AllenNLP: A Deep Semantic Natural Language Processing Platform. CoRR, Vol. abs\/1803.07640 (2018), 6 pages."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99740-7_21"},{"volume-title":"Mask R-CNN. In IEEE International Conference on Computer Vision (ICCV). IEEE","author":"He Kaiming","key":"e_1_3_2_2_15_1","unstructured":"Kaiming He , Georgia Gkioxari , Piotr Doll\u00e1 r, and Ross B. Girshick . 2017 . Mask R-CNN. In IEEE International Conference on Computer Vision (ICCV). IEEE , Venice, Italy, 2980--2988. Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1 r, and Ross B. Girshick. 2017. Mask R-CNN. In IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, Italy, 2980--2988."},{"key":"e_1_3_2_2_16_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE","author":"He Kaiming","year":"2016","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016 . Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Las Vegas, NV, USA, 770--778. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, 770--778."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/844379.844681"},{"key":"e_1_3_2_2_18_1","volume-title":"Kell","author":"Holzinger Andreas","year":"2017","unstructured":"Andreas Holzinger , Chris Biemann , Constantinos S. Pattichis , and Douglas B . Kell . 2017 . What do we need to build explainable AI systems for the medical domain? CoRR , Vol. abs\/ 1712 .09923 (2017), 28 pages. http:\/\/arxiv.org\/abs\/1712.09923 Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis, and Douglas B. Kell. 2017. What do we need to build explainable AI systems for the medical domain? CoRR, Vol. abs\/1712.09923 (2017), 28 pages. http:\/\/arxiv.org\/abs\/1712.09923"},{"key":"e_1_3_2_2_19_1","volume-title":"Strongly Consistent Metadata. In 37th IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE","author":"Ismail Mahmoud","year":"2017","unstructured":"Mahmoud Ismail , Ermias Gebremeskel , Theofilos Kakantousis , Gautier Berthou , and Jim Dowling . 2017 . Hopsworks: Improving User Experience and Development on Hadoop with Scalable , Strongly Consistent Metadata. In 37th IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE , Atlanta, GA, USA, 2525--2528. Mahmoud Ismail, Ermias Gebremeskel, Theofilos Kakantousis, Gautier Berthou, and Jim Dowling. 2017. Hopsworks: Improving User Experience and Development on Hadoop with Scalable, Strongly Consistent Metadata. In 37th IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE, Atlanta, GA, USA, 2525--2528."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330648"},{"volume-title":"Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations (ICLR). ICLR Press","author":"Diederik","key":"e_1_3_2_2_21_1","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015 . Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations (ICLR). ICLR Press , San Diego, CA, USA, 15 pages. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations (ICLR). ICLR Press, San Diego, CA, USA, 15 pages."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3122009.3122034"},{"key":"e_1_3_2_2_23_1","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_2_24_1","volume-title":"7th ICML Workshop on Automated Machine Learning (AutoML). ACM, Virtual Conference, 16 pages.","author":"LeDell Erin","year":"2020","unstructured":"Erin LeDell and Sebastien Poirier . 2020 . H2O AutoML: Scalable Automatic Machine Learning . In 7th ICML Workshop on Automated Machine Learning (AutoML). ACM, Virtual Conference, 16 pages. Erin LeDell and Sebastien Poirier. 2020. H2O AutoML: Scalable Automatic Machine Learning. In 7th ICML Workshop on Automated Machine Learning (AutoML). ACM, Virtual Conference, 16 pages."},{"volume-title":"An ADMM Based Framework for AutoML Pipeline Configuration. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). AAAI","author":"Liu Sijia","key":"e_1_3_2_2_25_1","unstructured":"Sijia Liu , Parikshit Ram , Deepak Vijaykeerthy , Djallel Bouneffouf , Gregory Bramble , Horst Samulowitz , Dakuo Wang , Andrew Conn , and Alexander G. Gray . 2020 . An ADMM Based Framework for AutoML Pipeline Configuration. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). AAAI , New York, NY, USA, 4892--4899. Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, and Alexander G. Gray. 2020. An ADMM Based Framework for AutoML Pipeline Configuration. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). AAAI, New York, NY, USA, 4892--4899."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/2336664.2336676"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00051"},{"key":"e_1_3_2_2_28_1","volume-title":"Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, and Mich\u00e8 le Sebag.","author":"Madrid Jorge G.","year":"2019","unstructured":"Jorge G. Madrid , Hugo Jair Escalante , Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, and Mich\u00e8 le Sebag. 2019 . Towards AutoML in the presence of Drift: first results. CoRR , Vol. abs\/ 1907 .10772 (2019), 14 pages. http:\/\/arxiv.org\/abs\/1907.10772 Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, and Mich\u00e8 le Sebag. 2019. Towards AutoML in the presence of Drift: first results. CoRR, Vol. abs\/1907.10772 (2019), 14 pages. http:\/\/arxiv.org\/abs\/1907.10772"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2807410"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2011.03.018"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/2933923.2933973"},{"key":"e_1_3_2_2_34_1","volume-title":"RISE: Randomized Input Sampling for Explanation of Black-box Models. In British Machine Vision Conference 2018 (BMVC). British Machine Vision Association","author":"Petsiuk Vitali","year":"2018","unstructured":"Vitali Petsiuk , Abir Das , and Kate Saenko . 2018 . RISE: Randomized Input Sampling for Explanation of Black-box Models. In British Machine Vision Conference 2018 (BMVC). British Machine Vision Association , Newcastle, UK, 151. Vitali Petsiuk, Abir Das, and Kate Saenko. 2018. RISE: Randomized Input Sampling for Explanation of Black-box Models. In British Machine Vision Conference 2018 (BMVC). British Machine Vision Association, Newcastle, UK, 151."},{"key":"e_1_3_2_2_35_1","volume-title":"YOLOv3: An Incremental Improvement. CoRR","author":"Redmon Joseph","year":"2018","unstructured":"Joseph Redmon and Ali Farhadi . 2018. YOLOv3: An Incremental Improvement. CoRR , Vol. abs\/ 1804 .02767 ( 2018 ). arxiv: 1804.02767 http:\/\/arxiv.org\/abs\/1804.02767 Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. CoRR, Vol. abs\/1804.02767 (2018). arxiv: 1804.02767 http:\/\/arxiv.org\/abs\/1804.02767"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2946162"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_2_39_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations (ICLR). ICLR Press","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman . 2015 . Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations (ICLR). ICLR Press , San Diego, CA, USA, 15 pages. Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations (ICLR). ICLR Press, San Diego, CA, USA, 15 pages."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999325.2999464"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300831"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.7326\/M19-2548"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.14778\/3282495.3282499"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-015-0391-4"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3003665.3003669"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2018-1456"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3225058.3225069"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.5555\/3485849.3485854"},{"key":"e_1_3_2_2_49_1","volume-title":"Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR","author":"Zimmer Lucas","year":"2020","unstructured":"Lucas Zimmer , Marius Lindauer , and Frank Hutter . 2020. Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR , Vol. abs\/ 2006 .13799 ( 2020 ), 1--15. Lucas Zimmer, Marius Lindauer, and Frank Hutter. 2020. Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR, Vol. abs\/2006.13799 (2020), 1--15."}],"event":{"name":"MM '21: ACM Multimedia Conference","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Virtual Event China","acronym":"MM '21"},"container-title":["Proceedings of the 29th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474085.3475176","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474085.3475176","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:47Z","timestamp":1750193327000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474085.3475176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,17]]},"references-count":50,"alternative-id":["10.1145\/3474085.3475176","10.1145\/3474085"],"URL":"https:\/\/doi.org\/10.1145\/3474085.3475176","relation":{},"subject":[],"published":{"date-parts":[[2021,10,17]]},"assertion":[{"value":"2021-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}