{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:18:57Z","timestamp":1767964737688,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"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":[[2021,6,9]]},"DOI":"10.1145\/3448016.3457236","type":"proceedings-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T17:22:39Z","timestamp":1624036959000},"page":"2404-2409","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Agile and Accurate CTR Prediction Model Training for Massive-Scale Online Advertising Systems"],"prefix":"10.1145","author":[{"given":"Zhiqiang","family":"Xu","sequence":"first","affiliation":[{"name":"Baidu Research, Beijing, China"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"Baidu Search Ads (Phoenix Nest), Beijing, China"}]},{"given":"Weijie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Baidu Research, Bellevue, WA, USA"}]},{"given":"Xing","family":"Shen","sequence":"additional","affiliation":[{"name":"Baidu Search Ads (Phoenix Nest), Beijing, China"}]},{"given":"Tianbo","family":"Huang","sequence":"additional","affiliation":[{"name":"Baidu Search Ads (Phoenix Nest), Beijing, China"}]},{"given":"Xiaoyun","family":"Li","sequence":"additional","affiliation":[{"name":"Baidu Research, Bellevue, WA, USA"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[{"name":"Baidu Research, Bellevue, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"An electronic digital computor using cold cathode counting tubes for storage. Electronic Engineering","author":"Barnes RCM","year":"1951","unstructured":"RCM Barnes , EH Cooke-Yarborough , and DGA Thomas . 1951. An electronic digital computor using cold cathode counting tubes for storage. Electronic Engineering ( 1951 ). RCM Barnes, EH Cooke-Yarborough, and DGA Thomas. 1951. An electronic digital computor using cold cathode counting tubes for storage. Electronic Engineering (1951)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/792550.792552"},{"key":"e_1_3_2_2_3_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning (ICML)","author":"Chen Wenlin","year":"2015","unstructured":"Wenlin Chen , James T. Wilson , Stephen Tyree , Kilian Q. Weinberger , and Yixin Chen . 2015 . Compressing Neural Networks with the Hashing Trick . In Proceedings of the 32nd International Conference on Machine Learning (ICML) . Lille, France, 2285--2294. Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, and Yixin Chen. 2015. Compressing Neural Networks with the Hashing Trick. In Proceedings of the 32nd International Conference on Machine Learning (ICML). Lille, France, 2285--2294."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966159"},{"key":"e_1_3_2_2_5_1","volume-title":"Proceedings of Machine Learning and Systems (MLSys)","author":"Choi Jungwook","year":"2019","unstructured":"Jungwook Choi , Swagath Venkataramani , Vijayalakshmi Srinivasan , Kailash Gopalakrishnan , Zhuo Wang , and Pierce Chuang . 2019 . Accurate and Efficient 2-bit Quantized Neural Networks . In Proceedings of Machine Learning and Systems (MLSys) . Stanford, CA. Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, and Pierce Chuang. 2019. Accurate and Efficient 2-bit Quantized Neural Networks. In Proceedings of Machine Learning and Systems (MLSys). Stanford, CA."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2901318.2901323"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.97.1.242"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/bult.1720320206"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330651"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412162"},{"key":"e_1_3_2_2_11_1","first-page":"61","article-title":"Round-off errors in numerical integration on automatic machinery-preliminary report","volume":"56","author":"Forsythe George E","year":"1950","unstructured":"George E Forsythe . 1950 . Round-off errors in numerical integration on automatic machinery-preliminary report . In Bulletin of the American Mathematical Society , Vol. 56. 61 -- 61 . George E Forsythe. 1950. Round-off errors in numerical integration on automatic machinery-preliminary report. In Bulletin of the American Mathematical Society , Vol. 56. 61--61.","journal-title":"Bulletin of the American Mathematical Society"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1137\/1001011"},{"key":"e_1_3_2_2_13_1","volume-title":"Proceedings of the International Conference on Field-Programmable Technology (FPT)","author":"Fox Sean","unstructured":"Sean Fox , Julian Faraone , David Boland , Kees A. Vissers , and Philip H. W. Leong . 2019. Training Deep Neural Networks in Low-Precision with High Accuracy Using FPGAs . In Proceedings of the International Conference on Field-Programmable Technology (FPT) . Tianjin, China, 1--9. Sean Fox, Julian Faraone, David Boland, Kees A. Vissers, and Philip H. W. Leong. 2019. Training Deep Neural Networks in Low-Precision with High Accuracy Using FPGAs. In Proceedings of the International Conference on Field-Programmable Technology (FPT) . Tianjin, China, 1--9."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104326"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning (ICML)","author":"Gupta Suyog","year":"2015","unstructured":"Suyog Gupta , Ankur Agrawal , Kailash Gopalakrishnan , and Pritish Narayanan . 2015 . Deep Learning with Limited Numerical Precision . In Proceedings of the 32nd International Conference on Machine Learning (ICML) . Lille, France, 1737--1746. Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015. Deep Learning with Limited Numerical Precision. In Proceedings of the 32nd International Conference on Machine Learning (ICML). Lille, France, 1737--1746."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2648584.2648589"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196894"},{"key":"e_1_3_2_2_20_1","volume-title":"Deep Cross-Modal Hashing. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Jiang Qing-Yuan","year":"2017","unstructured":"Qing-Yuan Jiang and Wu-Jun Li . 2017 . Deep Cross-Modal Hashing. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Honolulu, HI, 3270--3278. Qing-Yuan Jiang and Wu-Jun Li. 2017. Deep Cross-Modal Hashing. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, 3270--3278."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3054185"},{"key":"e_1_3_2_2_22_1","volume-title":"Deep Signal Recovery with One-bit Quantization. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Khobahi Shahin","unstructured":"Shahin Khobahi , Naveed Naimipour , Mojtaba Soltanalian , and Yonina C. Eldar . 2019 . Deep Signal Recovery with One-bit Quantization. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Brighton, UK, 2987--2991. Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, and Yonina C. Eldar. 2019. Deep Signal Recovery with One-bit Quantization. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK, 2987--2991."},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Tan Yap-Peng","year":"2018","unstructured":"Jason Kuen, Xiangfei Kong, Zhe Lin, Gang Wang, Jianxiong Yin, Simon See, and Yap-Peng Tan . 2018 . Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks . In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Salt Lake City, UT, 7929--7938. Jason Kuen, Xiangfei Kong, Zhe Lin, Gang Wang, Jianxiong Yin, Simon See, and Yap-Peng Tan. 2018. Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, 7929--7938."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11713"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401238"},{"key":"e_1_3_2_2_26_1","volume-title":"Advances in Neural Information Processing Systems (NIPS).","author":"Li Hao","unstructured":"Hao Li , Soham De , Zheng Xu , Christoph Studer , Hanan Samet , and Tom Goldstein . 2017. Training Quantized Nets: A Deeper Understanding . In Advances in Neural Information Processing Systems (NIPS). Long Beach, CA , 5811--5821. Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein. 2017. Training Quantized Nets: A Deeper Understanding. In Advances in Neural Information Processing Systems (NIPS). Long Beach, CA, 5811--5821."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281241"},{"key":"e_1_3_2_2_28_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS).","author":"Li Ping","unstructured":"Ping Li , Xiaoyun Li , and Cun-Hui Zhang . 2019. Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling . In Advances in Neural Information Processing Systems (NeurIPS). Vancouver, Canada , 15900--15910. Ping Li, Xiaoyun Li, and Cun-Hui Zhang. 2019. Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling. In Advances in Neural Information Processing Systems (NeurIPS). Vancouver, Canada, 15900--15910."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16543"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371785"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"key":"e_1_3_2_2_32_1","volume-title":"Fixed Point Quantization of Deep Convolutional Networks. In Proceedings of the 33nd International Conference on Machine Learning (ICML)","volume":"48","author":"Lin Darryl Dexu","unstructured":"Darryl Dexu Lin , Sachin S. Talathi , and V. Sreekanth Annapureddy . 2016 . Fixed Point Quantization of Deep Convolutional Networks. In Proceedings of the 33nd International Conference on Machine Learning (ICML) , Vol. 48 . New York City, NY, 2849--2858. Darryl Dexu Lin, Sachin S. Talathi, and V. Sreekanth Annapureddy. 2016. Fixed Point Quantization of Deep Convolutional Networks. In Proceedings of the 33nd International Conference on Machine Learning (ICML), Vol. 48. New York City, NY, 2849--2858."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2063"},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR)","author":"Micikevicius Paulius","year":"2018","unstructured":"Paulius Micikevicius , Sharan Narang , Jonah Alben , Gregory F. Diamos , Erich Elsen , David Garc'i a, Boris Ginsburg , Michael Houston , Oleksii Kuchaiev , Ganesh Venkatesh , and Hao Wu . 2018 . Mixed Precision Training . In Proceedings of the 6th International Conference on Learning Representations (ICLR) . Vancouver, Canada. Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David Garc'i a, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu. 2018. Mixed Precision Training. In Proceedings of the 6th International Conference on Learning Representations (ICLR). Vancouver, Canada."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242643"},{"key":"e_1_3_2_2_36_1","volume-title":"Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA)","author":"Sa Christopher De","year":"2017","unstructured":"Christopher De Sa , Matthew Feldman , Christopher R\u00e9 , and Kunle Olukotun . 2017 . Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent . In Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA) . Toronto, Canada, 561--574. Christopher De Sa, Matthew Feldman, Christopher R\u00e9 , and Kunle Olukotun. 2017. Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent. In Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA). Toronto, Canada, 561--574."},{"key":"e_1_3_2_2_37_1","volume-title":"Proceedings of the 7th International Conference on Learning Representations (ICLR)","author":"Sakr Charbel","year":"2019","unstructured":"Charbel Sakr , Naigang Wang , Chia-Yu Chen , Jungwook Choi , Ankur Agrawal , Naresh R. Shanbhag , and Kailash Gopalakrishnan . 2019 . Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks . In Proceedings of the 7th International Conference on Learning Representations (ICLR) . New Orleans, LA. Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh R. Shanbhag, and Kailash Gopalakrishnan. 2019. Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks. In Proceedings of the 7th International Conference on Learning Representations (ICLR). New Orleans, LA."},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)","author":"Shan Ying","unstructured":"Ying Shan , T. Ryan Hoens , Jian Jiao , Haijing Wang , Dong Yu , and J. C. Mao . 2016. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features . In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) . San Francisco, CA, 255--262. Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and J. C. Mao. 2016. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). San Francisco, CA, 255--262."},{"key":"e_1_3_2_2_39_1","unstructured":"Anshumali Shrivastava and Ping Li. 2014. Asymmetric textLSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). In Advances in Neural Information Processing Systems (NIPS) . Montreal Canada 2321--2329.  Anshumali Shrivastava and Ping Li. 2014. Asymmetric textLSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). In Advances in Neural Information Processing Systems (NIPS) . Montreal Canada 2321--2329."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196930"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371830"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijindorg.2006.10.002"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553516"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/1516238"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00225"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403297"},{"key":"e_1_3_2_2_47_1","volume-title":"Systems for Machine Learning Workshop at NeurIPS","author":"Zhang Jian","year":"2018","unstructured":"Jian Zhang , Jiyan Yang , and Hector Yuen . 2018 . Training with low-precision embedding tables . In Systems for Machine Learning Workshop at NeurIPS . Montreal, Canada. Jian Zhang, Jiyan Yang, and Hector Yuen. 2018. Training with low-precision embedding tables. In Systems for Machine Learning Workshop at NeurIPS . Montreal, Canada."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00094"},{"key":"e_1_3_2_2_49_1","volume-title":"Proceedings of Machine Learning and Systems (MLSys)","author":"Zhao Weijie","year":"2020","unstructured":"Weijie Zhao , Deping Xie , Ronglai Jia , Yulei Qian , Ruiquan Ding , Mingming Sun , and Ping Li . 2020 b. Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems . In Proceedings of Machine Learning and Systems (MLSys) . Austin, TX. Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, and Ping Li. 2020 b. Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems. In Proceedings of Machine Learning and Systems (MLSys). Austin, TX."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358045"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.01.018"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_2_53_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS).","author":"Zhou Zhixin","unstructured":"Zhixin Zhou , Shulong Tan , Zhaozhuo Xu , and Ping Li. 2019. M\u00f6bius Transformation for Fast Inner Product Search on Graph . In Advances in Neural Information Processing Systems (NeurIPS). Vancouver, Canada , 8216--8227. Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, and Ping Li. 2019. M\u00f6bius Transformation for Fast Inner Product Search on Graph. In Advances in Neural Information Processing Systems (NeurIPS). Vancouver, Canada, 8216--8227."}],"event":{"name":"SIGMOD\/PODS '21: International Conference on Management of Data","location":"Virtual Event China","acronym":"SIGMOD\/PODS '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 2021 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3457236","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448016.3457236","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:05Z","timestamp":1750195685000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3457236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,9]]},"references-count":53,"alternative-id":["10.1145\/3448016.3457236","10.1145\/3448016"],"URL":"https:\/\/doi.org\/10.1145\/3448016.3457236","relation":{},"subject":[],"published":{"date-parts":[[2021,6,9]]},"assertion":[{"value":"2021-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}