Machine Learning with Requirements: a Manifesto

• URL: http://arxiv.org/abs/2304.03674v2
• Date: Fri, 2 Feb 2024 18:04:02 GMT
• Title: Machine Learning with Requirements: a Manifesto
• Authors: Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz
• Abstract summary: We argue that requirements definition and satisfaction can go a long way to make machine learning models even more fitting to the real world. We show how the requirements specification can be fruitfully integrated into the standard machine learning development pipeline.
• Score: 114.97965827971132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains. However, it is still an open issue how make them applicable to high-stakes or safety-critical application domains, as they can often be brittle and unreliable. In this paper, we argue that requirements definition and satisfaction can go a long way to make machine learning models even more fitting to the real world, especially in critical domains. To this end, we present two problems in which (i) requirements arise naturally, (ii) machine learning models are or can be fruitfully deployed, and (iii) neglecting the requirements can have dramatic consequences. We show how the requirements specification can be fruitfully integrated into the standard machine learning development pipeline, proposing a novel pyramid development process in which requirements definition may impact all the subsequent phases in the pipeline, and viceversa.

Related papers

• PiShield: A PyTorch Package for Learning with Requirements [49.03568411956408]
  Deep learning models often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks' topology.
  arXiv  Detail & Related papers  (2024-02-28T12:24:27Z)
• Formal and Practical Elements for the Certification of Machine Learning Systems [0.9217021281095907]
  We show how parameters of machine learning models are not hand-coded nor derived from physics but learned from data. As a result, requirements cannot be directly traced to lines of code, hindering the current bottom-up aerospace certification paradigm. Based on a scalable statistical verifier, our proposed framework is model-agnostic and tool-independent.
  arXiv  Detail & Related papers  (2023-10-05T00:20:59Z)
• Learnware: Small Models Do Big [69.88234743773113]
  The prevailing big model paradigm, which has achieved impressive results in natural language processing and computer vision applications, has not yet addressed those issues, whereas becoming a serious source of carbon emissions. This article offers an overview of the learnware paradigm, which attempts to enable users not need to build machine learning models from scratch, with the hope of reusing small models to do things even beyond their original purposes.
  arXiv  Detail & Related papers  (2022-10-07T15:55:52Z)
• Machine Learning for Microcontroller-Class Hardware -- A Review [1.5311932971314297]
  This paper highlights the unique challenges of enabling onboard machine learning for microcontroller class devices. We introduce a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices.
  arXiv  Detail & Related papers  (2022-05-29T00:59:38Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• 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.