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US9165243B2 - Tensor deep stacked neural network - Google Patents US9165243B2 - Tensor deep stacked neural network - Google Patents Tensor deep stacked neural network Info Publication number US9165243B2 US9165243B2 US13/397,580 US80A US9165243B2 US 9165243 B2 US9165243 B2 US 9165243B2 US 80 A US80 A US 80A US 9165243 B2 US9165243 B2 US 9165243B2 Authority US United States Prior art keywords hidden units layer output comprises Prior art date 2012-02-15 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Active, expires 2032-12-02 Application number US13/397,580 Other versions Inventor Dong Yu Li Deng Brian Hutchinson Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.) Microsoft Technology Licensing LLC Original Assignee Microsoft Technology Licensing LLC Priority date (The priority date is an assumption and is not a legal conclusion.

The dsn file extension is commonly used by many various database applications. And possible program actions that can be done with the file: like open dsn file, edit dsn file, convert dsn file, view dsn file. Pdf) Search for file extension details and associated application(s) Find any file converter. A neat way of learning DSN weights. (Jozefowics, Zarembe, Sutskever, ICML 2015; Google. Convert speech acoustic waves into efficient & robust. Dense matrices. Sparse (often PDFs); can be dense.

Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.) 2012-02-15 Filing date 2012-02-15 Publication date 2015--02-15 Application filed by Microsoft Technology Licensing LLC filed Critical Microsoft Technology Licensing LLC 2012-02-15 Priority to US13/397,580 priority Critical patent/US9165243B2/en 2012-02-17 Assigned to MICROSOFT CORPORATION reassignment MICROSOFT CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DENG, LI, HUTCHINSON, BRIAN, YU, DONG 2013-08-15 Publication of US2A1 publication Critical patent/US2A1/en 2014-12-09 Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC reassignment MICROSOFT TECHNOLOGY LICENSING, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). 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. Recognizing speech based upon the outputs at the output units.