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In this report, we present a theoretical support of the continual learning method \textbf{Elastic Weight Consolidation}, introduced in paper titled `Overcoming catastrophic forgetting in neural networks'.
Uniqueness of the weights for minimal feedforward nets with a given input-output map
1992
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Fundamentals of statistical signal processing
1993
Earlier work this paper cites.
Lecture notes in
2013
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
2017
Earlier work this paper cites.
Learning without forgetting
2017
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Continual learning through synaptic intelligence
2017
Cited alongside, same era.
Memory aware synapses: Learning what (not) to forget
2018
Cited alongside, same era.
Rotating networks to prevent catastrophic forgetting (www.lherranz.org/2018/08/21/rotating-networks-to-prevent-catastrophic-forgetting/), 2018
2018
Cited alongside, same era.
Continuous learning in single-incremental-task scenarios
2019
Later among the works it cites.
Three scenarios for continual learning
2019
Later among the works it cites.
Localizing catastrophic forgetting in neural networks
2019
Later among the works it cites.
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