View source for 18727800. Partitioned Inference And Training Of Large Models (Google LLC)

=Partitioned Inference And Training Of Large Models=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Li Zhang of Kirkland WA US|Li Zhang of Kirkland WA US]][[Category:Li Zhang of Kirkland WA US]]

[[:Category:Matthew Sharifi of Kilchberg CH|Matthew Sharifi of Kilchberg CH]][[Category:Matthew Sharifi of Kilchberg CH]]

[[:Category:David Petrou of Brooklyn NY US|David Petrou of Brooklyn NY US]][[Category:David Petrou of Brooklyn NY US]]

[[:Category:Blaise Aguera Y Arcas of Seattle WA US|Blaise Aguera Y Arcas of Seattle WA US]][[Category:Blaise Aguera Y Arcas of Seattle WA US]]

==Partitioned Inference And Training Of Large Models==


This abstract first appeared for US patent application 18727800 titled 'Partitioned Inference And Training Of Large Models

==Original Abstract Submitted==

Systems and methods for partitioning a large model that has been configured to use a model-synthesis approach in which multiple basis models are combined to generate a final output. The present technology provides systems and methods for identifying a device-specific or subject-specific subset of those basis models to be used on a given device, such that it need not store the weight matrices for the entire set of basis models, and may perform inference using only the weight matrices of the identified subset of basis models. In some examples, the subset of basis models used by a given device may be updated based on actual usage and feedback. Likewise, in some examples, the model may be trained in a federated setting in which multiple devices each utilize different subsets of the basis models, and share training signals with a full copy of the model.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]