Overcoming catastrophic forgetting in neural networks

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possib...

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Detalhes bibliográficos
Publicado no:Proc Natl Acad Sci U S A
Main Authors: Kirkpatrick, James, Pascanu, Razvan, Rabinowitz, Neil, Veness, Joel, Desjardins, Guillaume, Rusu, Andrei A., Milan, Kieran, Quan, John, Ramalho, Tiago, Grabska-Barwinska, Agnieszka, Hassabis, Demis, Clopath, Claudia, Kumaran, Dharshan, Hadsell, Raia
Formato: Artigo
Idioma:Inglês
Publicado em: National Academy of Sciences 2017
Assuntos:
Biological Sciences
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC5380101/
https://ncbi.nlm.nih.gov/pubmed/28292907
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1611835114
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