SELM: Software Engineering of Machine Learning Models

• URL: http://arxiv.org/abs/2103.11249v1
• Date: Sat, 20 Mar 2021 21:43:24 GMT
• Title: SELM: Software Engineering of Machine Learning Models
• Authors: Nafiseh Jafari, Mohammad Reza Besharati, Mohammad Izadi, Maryam Hourali
• Abstract summary: In this article, we present a SELM framework for Software Engineering of machine Learning Models. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning. This issue highlights the importance of an interdisciplinary approach to machine learning.
• Score: 0.19116784879310023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning.

Related papers

• Towards a Domain-Specific Modelling Environment for Reinforcement Learning [0.13124513975412253]
  We use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling environment. We targeted reinforcement learning from the machine learning domain, and evaluated the proposed language, reinforcement learning modelling language (RLML) The tool supports syntax-directed editing, constraint checking, and automatic generation of code from RLML models.
  arXiv  Detail & Related papers  (2024-10-12T04:56:01Z)
• Application-Driven Innovation in Machine Learning [56.85396167616353]
  We describe the paradigm of application-driven research in machine learning. We show how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation.
  arXiv  Detail & Related papers  (2024-03-26T04:59:27Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Flashlight: Enabling Innovation in Tools for Machine Learning [50.63188263773778]
  We introduce Flashlight, an open-source library built to spur innovation in machine learning tools and systems. We see Flashlight as a tool enabling research that can benefit widely used libraries downstream and bring machine learning and systems researchers closer together.
  arXiv  Detail & Related papers  (2022-01-29T01:03:29Z)
• An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors [1.177923904173852]
  This report is to define a process for reproducing a state-of-the-art machine learning model at a level of quality suitable for inclusion in the Model Garden. We report on our experiences implementing the YOLO model family with a team of 26 student researchers, share the tools we developed, and describe the lessons we learned along the way.
  arXiv  Detail & Related papers  (2021-07-02T04:32:18Z)
• Pairing Conceptual Modeling with Machine Learning [1.7767466724342065]
  We provide an overview of machine learning foundations and development cycle. We propose a framework for incorporating conceptual modeling into data science projects. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs.
  arXiv  Detail & Related papers  (2021-06-27T15:06:59Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Integrating Machine Learning with Physics-Based Modeling [17.392391163553334]
  This article focuses on one particular issue of broad interest: How can we integrate machine learning with physics-based modeling? We discuss the two most important issues for developing machine learning-based physical models: Imposing physical constraints and obtaining optimal datasets. We end with a general discussion on where this integration will lead us, and where the new frontier will be after machine learning is successfully integrated into scientific modeling.
  arXiv  Detail & Related papers  (2020-06-04T02:35:10Z)
• Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
  The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
  arXiv  Detail & Related papers  (2020-05-27T11:56:56Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.