Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding

2013 ◽  
Author(s):  
Grégoire Mesnil ◽  
Xiaodong He ◽  
Li Deng ◽  
Yoshua Bengio
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Spoken Language ◽  
Network Architectures ◽  
Language Understanding ◽  
Learning Methods ◽  
Spoken Language Understanding ◽  
Neural Network Architectures

Optimizing Deep Learning Methods in Neural Network Architectures

2021 ◽  
Vol 14 (2) ◽  
pp. 93
Author(s):  
Kristina Gorshkova ◽  
Victoria Zueva ◽  
Maria Kuznetsova ◽  
Larisa Tugashova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architectures ◽  
Learning Methods ◽  
Neural Network Architectures

scholarly journals Reply to comment on: “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts”

2019 ◽  
Vol 26 (6) ◽  
pp. 580-581
Author(s):  
Anne Cocos ◽  
Alexander G Fiks ◽  
Aaron J Masino
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Peer Review ◽  
Adverse Drug Reactions ◽  
Recurrent Neural Network ◽  
Network Architectures ◽  
Drug Reactions ◽  
Detailed Review ◽  
Neural Network Architectures

Abstract We appreciate the detailed review provided by Magge et al1 of our article, “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.” 2 In their letter, they present a subjective criticism that rests on concerns about our dataset composition and potential misinterpretation of comparisons to existing methods. Our article underwent two rounds of extensive peer review and has been cited 28 times1 in the nearly 2 years since it was published online (February 2017). Neither the reviewers nor the citing authors raised similar concerns. There are, however, portions of the commentary that highlight areas of our work that would benefit from further clarification.

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