View source for 18348286. TECHNIQUES FOR HETEROGENEOUS CONTINUAL LEARNING WITH MACHINE LEARNING MODEL ARCHITECTURE PROGRESSION simplified abstract (NVIDIA Corporation)

=TECHNIQUES FOR HETEROGENEOUS CONTINUAL LEARNING WITH MACHINE LEARNING MODEL ARCHITECTURE PROGRESSION=


==Organization Name==

[[:Category:NVIDIA Corporation|NVIDIA Corporation]]


[[Category:NVIDIA Corporation]]

==Inventor(s)==

[[:Category:Hongxu Yin of San Jose CA (US)|Hongxu Yin of San Jose CA (US)]][[Category:Hongxu Yin of San Jose CA (US)]]

[[:Category:Wonmin Byeon of Santa Cruz CA (US)|Wonmin Byeon of Santa Cruz CA (US)]][[Category:Wonmin Byeon of Santa Cruz CA (US)]]

[[:Category:Jan Kautz of Lexington MA (US)|Jan Kautz of Lexington MA (US)]][[Category:Jan Kautz of Lexington MA (US)]]

[[:Category:Divyam Madaan of Brooklyn NY (US)|Divyam Madaan of Brooklyn NY (US)]][[Category:Divyam Madaan of Brooklyn NY (US)]]

[[:Category:Pavlo Molchanov of Mountain View CA (US)|Pavlo Molchanov of Mountain View CA (US)]][[Category: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 18348286 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 using data from another model.

* The method involves receiving a first data set and generating a second data set based on the first data set and a second machine learning model.
* The second data set includes features associated with tasks the second model was trained to perform.
* The first model is then trained based on 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 autonomous vehicles for improving the performance of machine learning models.

== Problems Solved ==
This technology addresses the challenge of training machine learning models with different architectures using data from existing models, potentially leading to better performance and generalization.

== Benefits ==
The method allows for leveraging the knowledge and features learned by an existing model to train a new model with a different architecture, potentially reducing the need for large amounts of labeled data.

== Potential Commercial Applications ==
* "Enhancing Machine Learning Model Training Using Transfer Learning"

== Possible Prior Art ==
There may be prior art related to transfer learning techniques in machine learning, where knowledge from one model is transferred to another for improved performance.

=== What are the specific tasks the second data set includes features for? ===

The second data set includes features associated with one or more tasks that the second machine learning model was previously trained to perform, which could include classification, regression, or other predictive tasks.

=== How does the method ensure the first model is effectively trained based on the second data set and the second machine learning model? ===

The method likely involves adjusting the parameters of the first model during training to minimize the difference between the model's predictions and the targets provided by the second data set. Additionally, techniques such as fine-tuning and regularization may be used to improve the model's performance.


==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.



[[Category:G06N20/00]]