Nvidia corporation (20240119361). TECHNIQUES FOR HETEROGENEOUS CONTINUAL LEARNING WITH MACHINE LEARNING MODEL ARCHITECTURE PROGRESSION simplified abstract

TECHNIQUES FOR HETEROGENEOUS CONTINUAL LEARNING WITH MACHINE LEARNING MODEL ARCHITECTURE PROGRESSION

Organization Name

nvidia corporation

Inventor(s)

Hongxu Yin of San Jose CA (US)

Wonmin Byeon of Santa Cruz CA (US)

Jan Kautz of Lexington MA (US)

Divyam Madaan of Brooklyn NY (US)

Pavlo Molchanov of Mountain View CA (US)

TECHNIQUES FOR HETEROGENEOUS CONTINUAL LEARNING WITH MACHINE LEARNING MODEL ARCHITECTURE PROGRESSION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119361 titled 'TECHNIQUES FOR HETEROGENEOUS CONTINUAL LEARNING WITH MACHINE LEARNING MODEL ARCHITECTURE PROGRESSION

Simplified Explanation

The abstract describes a method for training a machine learning model with a different architecture by generating a new data set based on the tasks the model was previously trained to perform.

• Explanation of the patent:

 * Receive a first data set.
 * Generate a second data set based on the first data set and a second machine learning model.
 * Train the first machine learning model using the second data set and the second machine learning model.

Potential Applications

This technology could be applied in various fields such as healthcare, finance, and marketing for improving the performance of machine learning models.

Problems Solved

This method helps in transferring knowledge from one machine learning model to another with a different architecture, enabling faster and more efficient training of new models.

Benefits

• Faster training of machine learning models.
• Improved performance of new models by leveraging knowledge from existing models.

Potential Commercial Applications

Optimizing marketing campaigns, improving medical diagnosis accuracy, enhancing financial forecasting models.

Possible Prior Art

There may be prior art related to transfer learning techniques in machine learning, where knowledge from one model is used to train another model. However, this specific method of generating a new data set based on the tasks of a pre-trained model may be a novel approach.

How does this method compare to traditional transfer learning techniques?

This article does not provide a direct comparison between this method and traditional transfer learning techniques in terms of performance, efficiency, or applicability to different types of machine learning models.

What are the potential limitations of this approach in real-world applications?

The article does not address any potential limitations or challenges that may arise when implementing this method in practical scenarios, such as scalability, data quality requirements, or computational resources needed.

Original Abstract Submitted

one embodiment of a method for training a first machine learning model having a different architecture than a second machine learning model includes receiving a first data set, performing one or more operations to generate a second data set based on the first data set and the second machine learning model, wherein the second data set includes at least one feature associated with one or more tasks that the second machine learning model was previously trained to perform, and performing one or more operations to train the first machine learning model based on the second data set and the second machine learning model.