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10.1073/pnas.1611835114 http://scihub22266oqcxt.onion/10.1073/pnas.1611835114 C5380101!5380101 !28292907     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28292907 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+Natl+Acad+Sci+U+S+A 2017 ; 114 (13 ): 3521-3526 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 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 possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games 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 ): 3521-3526 PMID28292907 show ga 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 possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. |*Neural Networks, Computer [MESH] |Algorithms [MESH] |Artificial Intelligence [MESH] |Computer Simulation [MESH] |Humans [MESH] |Learning [MESH] |Memory [MESH]Overcoming catastrophic forgetting in neural networks (epmc).pdf DeepDyve Pubget Overpricing