Book ; Online: Eliminating Meta Optimization Through Self-Referential Meta Learning
2022
Abstract: Meta Learning automates the search for learning algorithms. At the same time, it creates a dependency on human engineering on the meta-level, where meta learning algorithms need to be designed. In this paper, we investigate self-referential meta learning ...
| Abstract | Meta Learning automates the search for learning algorithms. At the same time, it creates a dependency on human engineering on the meta-level, where meta learning algorithms need to be designed. In this paper, we investigate self-referential meta learning systems that modify themselves without the need for explicit meta optimization. We discuss the relationship of such systems to in-context and memory-based meta learning and show that self-referential neural networks require functionality to be reused in the form of parameter sharing. Finally, we propose fitness monotonic execution (FME), a simple approach to avoid explicit meta optimization. A neural network self-modifies to solve bandit and classic control tasks, improves its self-modifications, and learns how to learn, purely by assigning more computational resources to better performing solutions. Comment: The first version appeared at ICML 2022, DARL Workshop |
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| Keywords | Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing ; Statistics - Machine Learning |
| Subject code | 006 |
| Publishing date | 2022-12-29 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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