@misc{nichol_first-order_2018,
	title = {On {First}-{Order} {Meta}-{Learning} {Algorithms}},
	url = {http://arxiv.org/abs/1803.02999},
	doi = {10.48550/arXiv.1803.02999},
	abstract = {This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates. This family includes and generalizes first-order MAML, an approximation to MAML obtained by ignoring second-order derivatives. It also includes Reptile, a new algorithm that we introduce here, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. We expand on the results from Finn et al. showing that first-order meta-learning algorithms perform well on some well-established benchmarks for few-shot classification, and we provide theoretical analysis aimed at understanding why these algorithms work.},
	urldate = {2023-10-03},
	publisher = {arXiv},
	author = {Nichol, Alex and Achiam, Joshua and Schulman, John},
	month = oct,
	year = {2018},
	note = {arXiv:1803.02999 [cs]},
	keywords = {Computer Science - Machine Learning},
}

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