US Patent Application 17738268. TRAINING A NEURAL NETWORK TO ACHIEVE AVERAGE CALIBRATION simplified abstract

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TRAINING A NEURAL NETWORK TO ACHIEVE AVERAGE CALIBRATION

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION


Inventor(s)

Hiroki Yanagisawa of Kawasaki (JP)

Toshiya Iwamori of Tokyo (JP)

Akira Koseki of Kanagawa-ken (JP)

Michiharu Kudo of Kamakura-shi (JP)

TRAINING A NEURAL NETWORK TO ACHIEVE AVERAGE CALIBRATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17738268 titled 'TRAINING A NEURAL NETWORK TO ACHIEVE AVERAGE CALIBRATION

Simplified Explanation

The patent application describes a method for training a neural network to analyze data in a way that meets average calibration standards.

  • The method involves manipulating a data set that includes an outcomes vector and a set of feature vectors.
  • The processor repeatedly selects a subset of the feature vectors and generates a distribution vector for the corresponding subset of outcomes.
  • A prediction vector is produced by running the neural network on the selected feature vectors.
  • The Bregman divergence between the distribution vector and a scoring distribution vector of the prediction vector is calculated.
  • The weights of the neural network are updated based on the Bregman divergence.


Original Abstract Submitted

A method, which trains a neural network to perform an analysis that satisfies average calibration, includes a processor manipulating a data set that includes an outcomes vector and a set of feature vectors, each of which corresponds to one of the outcomes in the outcomes vector. The processor repeatedly: selects a subset of the set of feature vectors; generates a distribution vector for a subset of the outcomes vector that corresponds to the subset of the set of feature vectors; produces a prediction vector by running the neural network on the subset of the set of feature vectors; calculates a Bregman divergence between the distribution vector and a scoring distribution vector of the prediction vector; and updates weights of the neural network based on the Bregman divergence.

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