Meta-Learning and representation learner: A short theoretical note
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910536464072704 |
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| author | Bouchattaoui, Mouad El |
| author_facet | Bouchattaoui, Mouad El |
| contents | Meta-learning, or "learning to learn," is a subfield of machine learning where the goal is to develop models and algorithms that can learn from various tasks and improve their learning process over time. Unlike traditional machine learning methods focusing on learning a specific task, meta-learning aims to leverage experience from previous tasks to enhance future learning. This approach is particularly beneficial in scenarios where the available data for a new task is limited, but there exists abundant data from related tasks. By extracting and utilizing the underlying structure and patterns across these tasks, meta-learning algorithms can achieve faster convergence and better performance with fewer data. The following notes are mainly inspired from \cite{vanschoren2018meta}, \cite{baxter2019learning}, and \cite{maurer2005algorithmic}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04189 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Meta-Learning and representation learner: A short theoretical note Bouchattaoui, Mouad El Machine Learning Statistics Theory Meta-learning, or "learning to learn," is a subfield of machine learning where the goal is to develop models and algorithms that can learn from various tasks and improve their learning process over time. Unlike traditional machine learning methods focusing on learning a specific task, meta-learning aims to leverage experience from previous tasks to enhance future learning. This approach is particularly beneficial in scenarios where the available data for a new task is limited, but there exists abundant data from related tasks. By extracting and utilizing the underlying structure and patterns across these tasks, meta-learning algorithms can achieve faster convergence and better performance with fewer data. The following notes are mainly inspired from \cite{vanschoren2018meta}, \cite{baxter2019learning}, and \cite{maurer2005algorithmic}. |
| title | Meta-Learning and representation learner: A short theoretical note |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2407.04189 |