Method for Meta-Level Continual Learning. Yu, H. & Munkhdalai, T. January 2019.
Method for Meta-Level Continual Learning [link]Paper  abstract   bibtex   
Classification of an input task data set by meta level continual learning includes analyzing first and second training data sets in a task space to generate first and second meta weights and a slow weight value, and comparing an input task data set to the slow weight to generate a fast weight. The first and second meta weights are parameterized with the fast weight value to update the slow weight value, whereby a value is associated with the input task data set, thereby classifying the input task data set by meta level continual learning.
@patent{yu_method_2019,
	title = {Method for {Meta}-{Level} {Continual} {Learning}},
	url = {https://patents.google.com/patent/US20190034798A1/en},
	abstract = {Classification of an input task data set by meta level continual learning includes analyzing first and second training data sets in a task space to generate first and second meta weights and a slow weight value, and comparing an input task data set to the slow weight to generate a fast weight. The first and second meta weights are parameterized with the fast weight value to update the slow weight value, whereby a value is associated with the input task data set, thereby classifying the input task data set by meta level continual learning.},
	nationality = {US},
	assignee = {University Of Massachusetts Medical School},
	number = {US20190034798A1},
	urldate = {2019-04-10},
	author = {Yu, Hong and Munkhdalai, Tsendsuren},
	month = jan,
	year = {2019},
	keywords = {loss, meta, slow, task, weight},
}

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