This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously learned information when new information is learned) in neural networks, and explores rehearsal mechanisms (the retraining of some of the previously learned information as the new information is added) as a potential ...
The author examines the problem of catastrophic forgetting-the overwriting of old information-in neural networks. He notes that R. Ratcliff's (1990) ...
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In this paper we examine the problem of catastrophic forgetting - the “overwriting” of old information - in neural networks. We note Ratcliffs (Ratclif ...
The author suggests that sweep rehearsal extends the approach of rehearsal mechanisms as far as is practicable, and exposes their eventual limitations.
Catastrophic forgetting in neural networks: the role of rehearsal ...
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The author examines the problem of catastrophic forgetting-the overwriting of old information-in neural networks. He notes that R. Ratcliff's (1990) experiments ...
A solution to the problem of catastrophic forgetting in neural networks is described, 'pseudorehearsal', a method which provides the advantages of rehearsal ...
Reviews the problem of catastrophic forgetting in neural networks, and explores rehearsal mechanisms as potential solution. Some experiments described by R.
This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously learned information when new information is learned) in ...
Oct 18, 2021 · Catastrophic forgetting is the name given to a common problem of machine learning models: when training on some new data from a new ...
May 6, 2024 · Rehearsal Mechanisms. One of the most effective approaches to reduce catastrophic forgetting is rehearsal. Rehearsal involves re-training the ...