View source for US Patent Application 18312584. METHODS, APPARATUS, AND ARTICLES OF MANUFACTURE TO RE-PARAMETERIZE MULTIPLE HEAD NETWORKS OF AN ARTIFICIAL INTELLIGENCE MODEL simplified abstract

=METHODS, APPARATUS, AND ARTICLES OF MANUFACTURE TO RE-PARAMETERIZE MULTIPLE HEAD NETWORKS OF AN ARTIFICIAL INTELLIGENCE MODEL=


==Organization Name==

[[Intel Corporation]]


==Inventor(s)==

[[Vinnam Kim of Pyeongtaek-si (KR)]]

[[Wonju Lee of Seongnam-si (KR)]]

[[Seok-Yong Byun of Seoul (KR)]]

==METHODS, APPARATUS, AND ARTICLES OF MANUFACTURE TO RE-PARAMETERIZE MULTIPLE HEAD NETWORKS OF AN ARTIFICIAL INTELLIGENCE MODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18312584 titled 'METHODS, APPARATUS, AND ARTICLES OF MANUFACTURE TO RE-PARAMETERIZE MULTIPLE HEAD NETWORKS OF AN ARTIFICIAL INTELLIGENCE MODEL

==Simplified Explanation==

The patent application describes a method and apparatus for re-parameterizing multiple head networks of an artificial intelligence (AI) model. 

* The invention involves training an AI model using both labeled data and pseudo-labeled data.
* The AI model includes multiple head networks.
* After the AI model has been trained, the invention re-parameterizes only the multiple head networks into a fully connected layer.
* Other portions of the AI model are not re-parameterized.
* This re-parameterization process helps optimize the performance of the AI model.
* The invention provides a more efficient and effective way to re-parameterize multiple head networks in an AI model.


==Original Abstract Submitted==

Systems, apparatus, articles of manufacture, and methods are disclosed re-parameterize multiple head networks of an artificial intelligence model. An example apparatus is to train an AI model using labeled data and pseudo-labeled data, the AI model including multiple head networks. Additionally, the example apparatus is to, after the AI model has been trained, re-parameterize the multiple head networks of the AI model into a fully connected layer without re-parameterizing other portions of the AI model.