The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 09, 2021

Filed:

Nov. 16, 2017
Applicant:

Mitusbishi Electric Research Laboratories, Inc., Cambridge, MA (US);

Inventors:

Ziming Zhang, Cambridge, MA (US);

Matthew Brand, Newton, MA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06F 17/10 (2006.01); G06Q 10/04 (2012.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06N 3/082 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/0481 (2013.01); G06N 3/084 (2013.01); G06N 5/02 (2013.01); G06Q 10/04 (2013.01);
Abstract

A computer-based system trains a neural network by solving a double layer optimization problem. The system includes an input interface to receive an input to the neural network and labels of the input to the neural network; a processor to solve a double layer optimization to produce parameters of the neural network, and an output interface to output the parameters of the neural network. The double layer optimization includes an optimization of a first layer subject to an optimization of a second layer. The optimization of the first layer minimizes a difference between an output of the neural network processing the input and the labels of the input to the neural network, the optimization of the second layer minimizes a distance between a non-negative output vector of each layer and a corresponding input vector to each layer. The input vector of a current layer is a linear transformation of the non-negative output vector of the previous layer.


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