University of Twente Student Theses
Learning to Learn : Generalization of Evolved Plasticity Rules in Recurrent Neural Networks
Joosten, Wesley (2021) Learning to Learn : Generalization of Evolved Plasticity Rules in Recurrent Neural Networks.
|
PDF
388kB |
| Abstract: | In machine learning, one often looks towards biology to find mechanisms to replicate. One of these mechanisms we observe in biology is synaptic plasticity rules. Plasticity rules allow for an artificial neural network to learn new tasks. By changing synapse weights based on these rules, an agent can adapt itself. Choosing proper rules then becomes a new challenge. Previously, gradient descent has been used to optimize one such set of rules. We use an evolutionary algorithm, specifically evolution strategies, to learn rules for recurrent neural networks. These are algorithms based on biological evolution which do not require a measure of error and may thus be applicable to a wider set of problems. Our results show that this approach may have potential, obtaining better-than-chance performance on classification tasks. This results in a proof of concept that enables further research in this area. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/86841 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page