# Meta-Learning and representation learner: A short theoretical note

arXiv: https://arxiv.org/abs/2407.04189 · HTML: https://mlsanity.com/paper/2407.04189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)

## Abstract (arXiv)

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 citevanschoren2018meta, citebaxter2019learning, and citemaurer2005algorithmic.