首页|Study Data from University of Namur Update Knowledge of Engineering (Towards Better Transition Modeling In Recurrent Neural Networks: the Case of Sign Language Tokenization)
Study Data from University of Namur Update Knowledge of Engineering (Towards Better Transition Modeling In Recurrent Neural Networks: the Case of Sign Language Tokenization)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Network Daily News - Current studyresults on Engineering have been published. According to news reporting from Namur, Belgium, by NewsRxjournalists, research stated, “Recurrent neural networks (RNNs) are a popular family of models widely usedwhen facing sequential data such as videos. However, RNNs make assumptions about state transitionsthat could be damageable.”Financial supporters for this research include Walloon region, Fonds de la Recherche Scientifique -FNRS, Funds InBev-Baillet Latour, F.R.S.-FNRS EOS VeriLearn.
NamurBelgiumEuropeEngineeringHealth and MedicineManual CommunicationNetworksNeural NetworksRehabilitationRehabilitation of Hearing ImpairedSign LanguageTherapyUniversity of Namur
NETL
NSTL
