[ "夏正勋(1979- ),男,星环信息科技(上海)有限公司高级研究员,主要研究方向为大数据、数据库、人工智能、流媒体处理技术等" ]
[ "杨一帆(1985- ),男,博士,星环信息科技(上海)有限公司产品总监、首席科学家,主要研究方向为统计(统计计算、生存分析、时间序列和生物信息)、机器学习中图计算、强化学习等" ]
[ "罗圣美(1971- ),男,博士,星环信息科技(上海)有限公司大数据研究院院长,主要研究方向为大数据、并行计算、云存储、人工智能等" ]
[ "赵大超(1989- ),男,星环信息科技(上海)有限公司产品研发经理,主要研究方向为大数据、人工智能等" ]
[ "张燕(1985- ),女,星环信息科技(上海)有限公司大数据技术研究员,主要研究方向为大数据、人工智能等" ]
[ "唐剑飞(1986- ),男,星环信息科技(上海)有限公司大数据技术标准研究员,主要研究方向为大数据、数据库、图计算等" ]
网络首发:2020-11,
纸质出版:2020-11-15
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夏正勋, 杨一帆, 罗圣美, 等. 生成技术在人工智能平台中的应用探索[J]. 大数据, 2020,6(6):2020058-1.
Zhengxun XIA, Yifan YANG, Shengmei LUO, et al. Application and exploration of automatic generation technology in artificial intelligence platform[J]. Big Data Research, 2020, 6(6): 2020058-1.
夏正勋, 杨一帆, 罗圣美, 等. 生成技术在人工智能平台中的应用探索[J]. 大数据, 2020,6(6):2020058-1. DOI: 10.11959/j.issn.2096-0271.2020058.
Zhengxun XIA, Yifan YANG, Shengmei LUO, et al. Application and exploration of automatic generation technology in artificial intelligence platform[J]. Big Data Research, 2020, 6(6): 2020058-1. DOI: 10.11959/j.issn.2096-0271.2020058.
随着人工智能(AI)技术的发展,AI应用进入了快速普及期,面对快速增长的市场需求,AI平台有必要引入自动化方法提升开发效率。在分析生成技术研究进展、AI平台现状及挑战的基础上,基于生成技术实现了AI平台的前后端适配、性能优化、模型安全提升等核心工作的自动化。新方法可以根据上下文的需要,生成数据或代码,以一种更灵活的方式满足AI应用及内核优化的需求,避免了大量的手工工作,有效提升了开发效率,降低了开发成本。
With the development of artificial intelligence (AI) technology
AI applications have entered a period of rapid popularization
facing the rapidly growing market demand
it is necessary for AI platforms to use automated methods to improve development efficiency.Based on the analysis of the research progress of generation technology
the status quo and challenges of AI platform
based on generation technology
the automation of AI platform’s front and rear end adaptation
performance optimization
and model security enhancement were realized
which can generate data or code according to the needs of the context and to meet the requirements in a more flexible way.It can also avoid a lot of manual work and can effectively improve development efficiency and reduce development cost.
肖寒 . J2EE平台下代码自动生成技术研究 [J ] . 电脑知识与技术 , 2009 , 5 ( 20 ): 5421 - 5422 ,5434.
XIAO H . Study of code generation technology based on J2EE platform [J ] . Computer Knowledge and Technology , 2009 , 5 ( 20 ): 5421 - 5422 ,5434.
CHEN T Q , THIERRY M , JIANG Z H , et al . TVM:an automated end-to-end optimizing compiler for deep learning [C ] // The 13th USENIX Symposium on Operating Systems Design and Implementation.[S.l.:s.n] . 2018 : 578 - 594 .
梁青青 . 基于关键超参 数选择的监督式AutoML性能优化 [D ] . 贵州:贵州大学 , 2019 .
LIANG Q Q . Performance optimization of supervised AutoML based on key super parameter selection [D ] . Guizhou:Guizhou University , 2019 .
L EMKE C , BUDKA M , GABRYS B . Metalearning:a survey of trends and technologies [J ] . Artificial Intelligence Review , 2015 , 44 ( 1 ): 117 - 130 .
陈森朋 , 吴佳 , 陈修云 . 基于强化学习的超参数优化方法 [J ] . 小型微型计算机系统 , 2020 , 41 ( 4 ): 679 - 684 .
CHEN S P , WU J , CHEN X Y . Hyperparameter optimization method based on reinforcement learning [J ] . Journal of Chinese Mini-Micro Computer Systems , 2020 , 41 ( 4 ): 679 - 684 .
李玉娟 . 基于改进粒子群算法的深度学习超参数优化方法 [J ] . 信息通信 , 2020 ( 1 ): 52 - 53 ,55.
LI Y J . Deep learning hyperparameter optimization method based on improved particle swarm optimization [J ] . Information & Communications , 2020 ( 1 ): 52 - 53 ,55.
朱汇龙 , 刘晓燕 , 刘瑶 . 基于贝叶斯新型深度学习超参数优化的研究 [J ] . 数据通信 , 2019 ( 2 ): 35 - 38 ,46.
ZHU H L , LIU X Y , LIU Y . Research on new deep learning super parameter optimization based on Bayes [J ] . Shuju Rongkin , 2019 ( 2 ): 35 - 38 ,46.
孙晓璇 , 张磊 , 李健 . 目标检测数据集半自动生成技术研究 [J ] . 计算机系统应用 , 2019 , 28 ( 10 ): 8 - 14 .
SUN X X , ZHANG L , LI J . Research on semiautomatic generation technology of object detection datasets [J ] . Computer Systems &Applications , 2019 , 28 ( 10 ): 8 - 14 .
杨懿男 , 齐林海 , 王红 , 等 . 基于生成对抗网络的小样本数据生成技术研究 [J ] . 电力建设 , 2019 , 40 ( 5 ): 71 - 77 .
YANG Y N , QI L H , WANG H , et al . Research on generation technology of small sample data based on generative adversarial network [J ] . Electric Power Construction , 2019 , 40 ( 5 ): 71 - 77 .
KURAKIN A , GOODFELLOW I , BENGIO S . Advers arial machine learning at scale [C ] // International Conference on Learning Representations.[S.l.:s.n] . 2017 .
SU P H , LIU Y H , SONG X . Research on intrusion detection method based on improved smote and XGBoost [C ] // The 8th International Conference on Communication and Network Security.[S.l.:s.n] . 2018 : 37 - 41 .
GOODFELLOW I J , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples [J ] . arXiv preprint,2015,arXiv:1412.6572 ,
CARLINI N , WAGNER D . Towards evaluating the robustness of neural networks [J ] . arXiv preprint,2016,arXiv:1608.04644 ,
MOOSAVI-DEZFOOLI S M , FAWZI A , FROSSARD P . DeepFool:a simple and accurate method to fool deep neural networks [C ] // The IEEE Computer Vision& Pattern Recognition . Piscataway:IEEE Press , 2016 : 2574 - 2582 .
SARKAR S , BANSAL A , MAHBUB U , et al . UPSET and ANGRI:breaking high performance image classifiers [J ] . arXiv preprint,2017,arXiv:1707.01159 ,
CISSEM ADIY NEVEROVAN . Houdini:fooling deep structured prediction models [J ] . arXiv preprint,2017,arXiv:1707.05373 ,
SU J W , VARGAS D V , KOUICHI S . One pixel attack for fooling deep neural networks [J ] . arXiv preprint,2017,arXiv:1710.08864 ,
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