GENO - Optimization for Classical Machine Learning Made Fast and Easy

被引:0
|
作者
Laue, Soeren [1 ,2 ]
Mitterreiter, Matthias [1 ]
Giesen, Joachim [1 ]
机构
[1] Friedrich Schiller Univ Jena, Fac Math & Comp Sci, Ernst Abbe Pl 2, D-07743 Jena, Germany
[2] Data Assessment Solut GmbH, Hannover, Germany
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most problems from classical machine learning can be cast as an optimization problem. We introduce GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver, i.e., Python code, that can solve this class of optimization problems. The generated solver is usually as fast as handwritten, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific problem class. An online interface to our framework can be found at http://www.geno-project.org.
引用
收藏
页码:13620 / 13621
页数:2
相关论文
共 50 条
  • [31] Active learning of biochemistry made easy (for the teacher)
    Bobich, Joseph A.
    JOURNAL OF CHEMICAL EDUCATION, 2008, 85 (02) : 234 - 236
  • [32] Quasar: Easy Machine Learning for Biospectroscopy
    Toplak, Marko
    Read, Stuart T.
    Sandt, Christophe
    Borondics, Ferenc
    CELLS, 2021, 10 (09)
  • [33] Machine Learning Made Easy: A Review of Scikit-learn Package in Python']Python Programming Language
    Hao, Jiangang
    Ho, Tin Kam
    JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS, 2019, 44 (03) : 348 - 361
  • [34] Combining Machine Learning and Classical Optimization Techniques in Vehicle to Vehicle Communication Network
    Hamdan, Mutasem
    Hamdi, Khairi
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2019, PT I, 2019, 11871 : 350 - 358
  • [35] A Fast Design and Optimization Method Based on Surrogate Model and Machine Learning
    Li, Wen Xi
    Li, Ying
    Yan, Ran
    Luo, Yong
    IVEC 2021: 2021 22ND INTERNATIONAL VACUUM ELECTRONICS CONFERENCE, 2021,
  • [36] Learning Automation Made Easy through Virtual Labs
    Narayanan, Geetha
    Deshpande, Anjali
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON LEARNING AND TEACHING IN COMPUTING AND ENGINEERING (LATICE 2016), 2016, : 60 - 65
  • [37] A machine learning-assisted structural optimization scheme for fast-tracking topology optimization
    Yi Xing
    Liyong Tong
    Structural and Multidisciplinary Optimization, 2022, 65
  • [38] A machine learning-assisted structural optimization scheme for fast-tracking topology optimization
    Xing, Yi
    Tong, Liyong
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2022, 65 (04)
  • [39] Full Protection Made Easy: The DisPath IP Fast Reroute Scheme
    Antonakopoulos, Spyridon
    Bejerano, Yigal
    Koppol, Pramod
    IEEE-ACM TRANSACTIONS ON NETWORKING, 2015, 23 (04) : 1229 - 1242
  • [40] MACHINE LEARNING MADE EASY: DESIGN OF A PROPOSAL FOR A MEDICAL STUDENT EDUCATIONAL SESSION ON HIGH YIELD STRATEGIES FOR LEARNING AND APPLYING DATA SCIENCE
    Brown, Nolan
    Kuo, Cathleen
    Neil, Zachery
    Gendreau, Julian
    NEURO-ONCOLOGY, 2023, 25