Meta-Learning: Learning to Learn in Machine Learning

Authors

  • T.V.S. Padmaja Principal & Head, Department of English

Keywords:

Meta learning, Artificial Intelligence, Artificial General Intelligence, machine learning

Abstract

Within the deep learning domain, one of the most active study areas is meta-learning. There are several perspectives within the Artificial Intelligence (AI) community that support the hypothesis that meta-learning is a stepping stone towards the development of Artificial General Intelligence (AGI). Recent years have witnessed an increase in the creative development of meta-learning systems. The most recent research results are compiled in this book to provide readers a comprehensive grasp of meta-learning and how it could affect different machine learning applications. With the help of this technological description, meta-learning and its applicability to real-world problems are intended to progress.

References

[1] A. K. Chakraverti, "Learning How to Learn : Meta Learning Approach to Improve Deep Learning," vol. 8, no. 10, pp. 1-6, 2020.

[2] M. Huisman, J. N. Van Rijn, and A. Plaat, A survey of deep meta-learning Springer Netherlands, 2021.

https://doi.org/10.1007/s10462-021-10004-4

[3] Y. Duan et al., "Learning to Diagnose : Meta-Learning for Efficient Adaptation in Few-Shot AIOps Scenarios," pp. 1-16, 2024.

https://doi.org/10.3390/electronics13112102

[4] A. Vettoruzzo and M. Bouguelia, "Advances and Challenges in Meta-Learning : A Technical Review," 2023.

[5] Q. Cheng et al., "AI for IT Operations ( AIOps ) on Cloud Platforms : Reviews , Opportunities and Challenges," pp. 1-34, 2023.

[6] H. Wang, "AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model," 2020 10th Annu. Comput. Commun. Work. Conf., pp. 417-423, 2022, https://doi.org/10.1109/CCWC47524.2020.9031232

[7] J. M. Kudari and S. Hembram, "Meta Learning challenges and Benefits," vol. 8, no. 6, pp. 1-5, 2021.

[8] T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, "Meta-Learning in Neural Networks : A Survey," pp. 1-20, 2021, , vol. 54, no. 6

https://doi.org/10.1109/TPAMI.2021.3079209

Downloads

Published

2024-06-24

How to Cite

[1]
Padmaja, T. 2024. Meta-Learning: Learning to Learn in Machine Learning. AG Volumes. 1, 1 (Jun. 2024), 1–5.