Understanding deep meta-learning

Huisman, M. (2024)

Doctoral Thesis

The invention of neural networks marks a critical milestone in the pursuit of true artificial intelligence. Despite their impressive performance on various tasks, these networks face limitations in learning efficiently as they are often trained from scratch. Deep meta-learning is one approach to improve the learning efficiency by leveraging prior knowledge and experience. Whilst many succesful deep meta-learning techniques have been proposed, our understanding of the performance of these methods remains limited. In this dissertation, we delve deeper into the underlying principles of these algorithms, and aim to gain a comprehensive understanding of why certain algorithms succeed while others fall short. This allows us to design enhanced deep meta-learning algorithms and reason about the impact of specific design choices on the performance of different algorithms. Moreover, we investigate the integration of theoretical principles into meta-learning algorithms to improve their performance. Overall, we make a small step toward a better understanding of deep meta-learning algorithms, paving the way for more robust and principled meta-learning techniques with broader applicability and superior performance.

• Meta-learning
• Few-shot learning
• Transfer learning
• Deep learning
• Neural networks

• All authors: Huisman, M.
• Supervisor: Plaat, A.
• Co-supervisor: Rijn, J.N. van
• Committee: Kleijn, H.C.M.; Batenburg, K.J.; Postma, E.O.; Moerland, T.M.; Magliacane, S.
• Qualification: Doctor (dr.)
• Awarding Institution: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
• Date: 2024-01-17
• Title of host publication: SIKS Dissertation Series
• ISBN (print): 9789493330436
• Name: 2024-04