NEURAL NETWORK LEARNING DEVICE, NEURAL NETWORK LEARNING METHOD, AND PROGRAM

Title:

NEURAL NETWORK LEARNING DEVICE, NEURAL NETWORK LEARNING METHOD, AND PROGRAM

Document Type and Number:

WIPO Patent Application WO/2019/208564

Kind Code:

A1

Abstract:

Provided is a neural network learning device capable of adjusting reduction amount of a model size. The neural network learning device comprises a group parameter generating unit that separates model parameters of a neural network model into arbitrarily defined groups and generates group parameters representing features of the respective groups, a regularization term calculating unit that calculates a regularization term under the assumption that distribution of the group parameters conforms to distribution defined by a hyperparameter as a parameter defining the feature of the distribution, and a model updating unit that calculates loss function from a correct-answer label in teacher data, output probability distribution obtained by inputting feature quantity corresponding to the correct-answer label in the teacher data into the neural network model, and the regularization term and updates the neural network model so as to reduce a value of the loss function.

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Inventors:

MORIYA TAKAFUMI (JP)
YAMAGUCHI YOSHIKAZU (JP)

Application Number:

PCT/JP2019/017216

Publication Date:

October 31, 2019

Filing Date:

April 23, 2019

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Assignee:

NIPPON TELEGRAPH & TELEPHONE (JP)

International Classes:

G06N3/08

Other References:

SCARDAPANE, S. ET AL., GROUP SPARSE REGULARIZATION FOR DEEP NEURAL NETWORKS, 2 July 2016 (2016-07-02), pages 1 - 10, XP029946330, Retrieved from the Internet [retrieved on 20190610]
U, X. F. ET AL., BAYESIAN VARIABLE SELECTION AND ESTIMATION FOR GROUP LASSO, vol. 10, no. 4, 3 December 2015 (2015-12-03), pages 909 - 936, XP055648755, Retrieved from the Internet [retrieved on 20190610]

Attorney, Agent or Firm:

NAKAO, Naoki et al. (JP)

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