Submitted by Decadz t3_109xqw3 in MachineLearning
There are quite a few papers on optimisation-based meta-learning approaches for learning parameter initialisations (i.e. MAML and its derivatives) [1, 2], and there are also many papers on learning optimisers [3].
Question: Are there any papers which combine the two?
I am aware of some papers such as [4, 5] which achieve this in some capacity indirectly/implicitly, but wondering if there are any other papers that I am not aware of, or do this explicitly? Thanks in advance.
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[1] Finn, C., et al. (2017. Model-agnostic meta-learning for fast adaptation of deep networks. ICML.)
[2] Nichol, A., et al. (2018. On first-order meta-learning algorithms.)
[3] Andrychowicz, M., et al. (2016. Learning to learn by gradient descent by gradient descent.) NIPS
[4] Li, Z., et al. (2017. Meta-sgd: Learning to learn quickly for few-shot learning.)
[5] Ravi, S., & Larochelle, H. (2016. Optimization as a model for few-shot learning. ICLR.)
Jumbofive t1_j41lc4f wrote
Is this close to what you are looking for? https://arxiv.org/abs/2211.09760