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1. 发明授权

US09165243B2 Tensor deep stacked neural network 有权
标题翻译：张量深层神经网络
公开(公告)号：US09165243B2
公开(公告)日：2015-10-20
申请号：US13397580
申请日：2012-02-15
申请人： Dong Yu , Li Deng , Brian Hutchinson
发明人： Dong Yu , Li Deng , Brian Hutchinson
IPC分类号： G06N3/04 , G06N3/08
CPC分类号： G06N3/04 , G06N3/08
摘要： A tensor deep stacked neural (T-DSN) network for obtaining predictions for discriminative modeling problems. The T-DSN network and method use bilinear modeling with a tensor representation to map a hidden layer to the predication layer. The T-DSN network is constructed by stacking blocks of a single hidden layer tensor neural network (SHLTNN) on top of each other. The single hidden layer for each block then is separated or divided into a plurality of two or more sections. In some embodiments, the hidden layer is separated into a first hidden layer section and a second hidden layer section. These multiple sections of the hidden layer are combined using a product operator to obtain an implicit hidden layer having a single section. In some embodiments the product operator is a Khatri-Rao product. A prediction is made using the implicit hidden layer and weights, and the output prediction layer is consequently obtained.
摘要翻译：张量深层次神经（T-DSN）网络，用于获得鉴别建模问题的预测。 T-DSN网络和方法使用具有张量表示的双线性建模来将隐藏层映射到预测层。 T-DSN网络由单个隐层张量神经网络（SHLTNN）的堆叠堆叠构成。然后，每个块的单个隐藏层被分离或分成多个两个或更多个部分。在一些实施例中，隐藏层被分成第一隐藏层部分和第二隐藏层部分。使用产品运算符组合隐藏层的这些多个部分以获得具有单个部分的隐式隐藏层。在一些实施例中，产品操作者是Khatri-Rao产品。使用隐式隐层和权重进行预测，从而获得输出预测层。

2. 发明申请

US20130212052A1 TENSOR DEEP STACKED NEURAL NETWORK 有权
标题翻译：传感器深层堆叠神经网络
公开(公告)号：US20130212052A1
公开(公告)日：2013-08-15
申请号：US13397580
申请日：2012-02-15
申请人： Dong Yu , Li Deng , Brian Hutchinson
发明人： Dong Yu , Li Deng , Brian Hutchinson
IPC分类号： G06N3/04 , G06N3/08
CPC分类号： G06N3/04 , G06N3/08
摘要： A tensor deep stacked neural (T-DSN) network for obtaining predictions for discriminative modeling problems. The T-DSN network and method use bilinear modeling with a tensor representation to map a hidden layer to the predication layer. The T-DSN network is constructed by stacking blocks of a single hidden layer tensor neural network (SHLTNN) on top of each other. The single hidden layer for each block then is separated or divided into a plurality of two or more sections. In some embodiments, the hidden layer is separated into a first hidden layer section and a second hidden layer section. These multiple sections of the hidden layer are combined using a product operator to obtain an implicit hidden layer having a single section. In some embodiments the product operator is a Khatri-Rao product. A prediction is made using the implicit hidden layer and weights, and the output prediction layer is consequently obtained.
摘要翻译：张量深层次神经（T-DSN）网络，用于获得鉴别建模问题的预测。 T-DSN网络和方法使用具有张量表示的双线性建模来将隐藏层映射到预测层。 T-DSN网络由单个隐层张量神经网络（SHLTNN）的堆叠堆叠构成。然后，每个块的单个隐藏层被分离或分成多个两个或更多个部分。在一些实施例中，隐藏层被分成第一隐藏层部分和第二隐藏层部分。使用产品运算符组合隐藏层的这些多个部分以获得具有单个部分的隐式隐藏层。在一些实施例中，产品操作者是Khatri-Rao产品。使用隐式隐层和权重进行预测，从而获得输出预测层。

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