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10.1073/pnas.1611835114 http://scihub22266oqcxt.onion/10.1073/pnas.1611835114 C5380101!5380101!28292907     free     free     free       Proc+Natl+Acad+Sci+U+S+A 2017 ; 114 (13): 3521-6 Nephropedia Template TP {{tp|p=28292907|t=2017. Overcoming catastrophic forgetting in neural networks |pdf=|usr=}}{{28292907}} gab.com Text Overcoming catastrophic forgetting in neural networks JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=28292907 #FOAvip Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially. Twit Text FOAVip Overcoming catastrophic forgetting in neural networks ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=28292907 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28292907 #FOAvip English Wikipedia <ref>{{Cite pmid|28292907}}</ref> Overcoming catastrophic forgetting in neural networks #MMPMID28292907 Kirkpatrick J; Pascanu R; Rabinowitz N; Veness J; Desjardins G; Rusu AA; Milan K; Quan J; Ramalho T; Grabska-Barwinska A; Hassabis D; Clopath C; Kumaran D; Hadsell R Proc Natl Acad Sci U S A 2017[Mar]; 114 (13): 3521-6 PMID28292907show ga Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially. äOvercoming catastrophic forgetting in neural networks (epmc).pdf DeepDyve Pubget Overpricing