Kia. has filed a patent for a method involving meta-learning to train models. The process includes training a base learning model, a loss meta-learning model for each task, and a meta-learning model to optimize parameters. This innovation aims to enhance model training efficiency and effectiveness. GlobalData’s report on Kia gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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According to GlobalData’s company profile on Kia, Battery management systems was a key innovation area identified from patents. Kia's grant share as of January 2024 was 41%. Grant share is based on the ratio of number of grants to total number of patents.

Meta-learning model training method

Source: United States Patent and Trademark Office (USPTO). Credit: Kia Corp

A recently filed patent (Publication Number: US20240037411A1) outlines a method for training a model using meta-learning. The method involves training a base learning model, a loss meta-learning model used as a loss function for each task, and a meta-learning model used to optimize a parameter of the loss meta-learning model. The training process includes generating output data of the base learning model, defining a task state based on the output data, updating the parameter of the loss meta-learning model, and minimizing the loss value to update the base learning model parameter.

Furthermore, the patent describes the use of an internal loop for training the base learning model and the loss meta-learning model for each task, while the training of the meta-learning model is part of an external loop. The method involves generating output data, determining elementary loss values, and summing them for each task within the internal loop. The external loop then updates the parameters of the base learning model, the loss meta-learning model, and the meta-learning model to minimize the summed elementary loss value. Additionally, the patent mentions the inclusion of a multi-layer perception in the loss meta-learning model for enhanced performance. This innovative approach to training models using meta-learning showcases a sophisticated method that could potentially revolutionize machine learning processes.

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