US Patent Application 18246782. LEARNING MODEL OPTIMIZATION DEVICE, LEARNING MODEL OPTIMIZATION METHOD, AND LEARNING MODEL OPTIMIZATION PROGRAM simplified abstract

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LEARNING MODEL OPTIMIZATION DEVICE, LEARNING MODEL OPTIMIZATION METHOD, AND LEARNING MODEL OPTIMIZATION PROGRAM

Organization Name

Nippon Telegraph and Telephone Corporation


Inventor(s)

Daiki Chijiwa of Musashino-shi, Tokyo (JP)

Kenji Umakoshi of Musashino-shi, Tokyo (JP)

Tomohiro Inoue of Musashino-shi, Tokyo (JP)

Daigoro Yokozeki of Musashino-shi, Tokyo (JP)

LEARNING MODEL OPTIMIZATION DEVICE, LEARNING MODEL OPTIMIZATION METHOD, AND LEARNING MODEL OPTIMIZATION PROGRAM - A simplified explanation of the abstract

This abstract first appeared for US patent application 18246782 titled 'LEARNING MODEL OPTIMIZATION DEVICE, LEARNING MODEL OPTIMIZATION METHOD, AND LEARNING MODEL OPTIMIZATION PROGRAM

Simplified Explanation

The patent application describes a learning model optimization device that improves the efficiency of machine learning algorithms by optimizing a binarization matrix.

  • The device includes a binarization matrix setting unit that sets a binarization matrix with elements as either "0" or "1".
  • A transformed matrix setting unit sets a transformed matrix by multiplying each element of the parameter matrix with the corresponding element of the binarization matrix.
  • A learning unit performs machine learning using the transformed matrix and adjusts the numerical values of the binarization matrix to optimize it.
  • A re-randomization processing unit further adjusts the parameter matrix to improve the optimization process.


Original Abstract Submitted

A learning model optimization device includes a binarization matrix setting unit configured to set a binarization matrix m in which each element is a numerical value of “0” or “1” and a transformed matrix setting unit configured to set a transformed matrix M having, as an element, a product of each element of the parameter matrix and each element of the binarization matrix in the same row and the same column. The learning model optimization device further includes a learning unit configured to perform machine learning using the transformed matrix M and change a numerical value of each element of the binarization matrix m such that a result of the machine learning approaches teacher data, thereby optimizing the binarization matrix m, and a re-randomization processing unit configured to change again a parameter of the parameter matrix w.

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