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Declarative machine learning systems

Authors:

Piero Molino,

Christopher RéAuthors Info & Claims

Communications of the ACM, Volume 65, Issue 1

Pages 42 - 49

https://doi.org/10.1145/3475167

Published: 17 December 2021 Publication History

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Abstract

The future of machine learning will depend on it being in the hands of the rest of us.

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Rall DFraunholz TKöhler TKratzer FMayer MSchmorell DLarsen LGörick DHuber ASchuster A(2024)OS4ML: Open Space for Machine LearningFlexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order10.1007/978-3-031-74482-2_6(47-57)Online publication date: 9-Dec-2024
https://doi.org/10.1007/978-3-031-74482-2_6
Nilsson MSchelén OLindgren ABodin UPaniagua CDelsing JSandin F(2023)Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directionsFrontiers in Neuroscience10.3389/fnins.2023.107443917Online publication date: 17-Feb-2023
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Bonin CSimas HPereira MDal Mago AChagas P(2023)Leveraging machine learning for predicting and monitoring clogging in laser cladding processes: An exploration of neural sensorsJournal of Laser Applications10.2351/7.000115435:4Online publication date: 26-Sep-2023
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Index Terms

Declarative machine learning systems
1. Theory of computation
  1. Logic
    1. Constraint and logic programming
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

Communications of the ACM Volume 65, Issue 1

January 2022

106 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3507640

Editor:
Andrew A. Chien
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 17 December 2021

Published in CACM Volume 65, Issue 1

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https://doi.org/10.1007/978-3-031-74482-2_6
Nilsson MSchelén OLindgren ABodin UPaniagua CDelsing JSandin F(2023)Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directionsFrontiers in Neuroscience10.3389/fnins.2023.107443917Online publication date: 17-Feb-2023
https://doi.org/10.3389/fnins.2023.1074439
Bonin CSimas HPereira MDal Mago AChagas P(2023)Leveraging machine learning for predicting and monitoring clogging in laser cladding processes: An exploration of neural sensorsJournal of Laser Applications10.2351/7.000115435:4Online publication date: 26-Sep-2023
https://doi.org/10.2351/7.0001154
Oppenlaender J(2023)A taxonomy of prompt modifiers for text-to-image generationBehaviour & Information Technology10.1080/0144929X.2023.228653243:15(3763-3776)Online publication date: 28-Nov-2023
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Balakrishnan SKumar KJanet JBabu DLora C(2023)An emergence of technological aids using machine learning algorithms to curtail the mounting manifestation of dyspraxiaMultimedia Tools and Applications10.1007/s11042-023-16464-wOnline publication date: 26-Aug-2023
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Abstract

References

Cited By

Index Terms

Recommendations

Lifelong Machine Learning

Machine Learning: The State of the Art

Dyna: toward a self-optimizing declarative language for machine learning applications

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