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- W2893677349 abstract "Catastrophic forgetting, which means that old tasks are forgotten mostly when new tasks are learned, is a crucial problem of neural networks for autonomous robots. This problem is due to backpropagation overwrites all network parameters, and therefore, can be solved by not overwriting important parameters for the old tasks. Hence, regularization methods, represented by elastic weight consolidation, give the globally stable equilibrium points to the optimal parameters for the old tasks. They unfortunately aim to hold all parameters, even if the regularization is weak. This paper therefore proposes a regularization method, named Check regularization, to consolidate only the important parameters for the tasks and to initialize the other parameters preparing for the future tasks. Simulations with two tasks to be learned sequentially show that the proposed method outperforms the previous method under a condition where the interference between the tasks is severe." @default.
- W2893677349 created "2018-10-05" @default.
- W2893677349 creator A5051304187 @default.
- W2893677349 date "2018-01-01" @default.
- W2893677349 modified "2023-09-26" @default.
- W2893677349 title "Check Regularization: Combining Modularity and Elasticity for Memory Consolidation" @default.
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- W2893677349 doi "https://doi.org/10.1007/978-3-030-01421-6_31" @default.
- W2893677349 hasPublicationYear "2018" @default.
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