Research map / Learning theory and algorithms / Prediction and decision losses
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use CMA-ES instead of Adam as the meta-optimizer (ablation) | plasticity rule search (Oja's rule) · single rate neuron, growing network size | scalability with network size | Helped |
| increase inner-loop network size | meta-learning of plasticity rules · two-layer rate network | compute time to meta-convergence | Hurt |
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