The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 11, 2026

Filed:

May. 19, 2023
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Yutian Chen, Cambridge, GB;

Xingyou Song, Jersey City, NJ (US);

Chansoo Lee, Pittsburgh, PA (US);

Zi Wang, Cambridge, MA (US);

Qiuyi Zhang, Pittsburgh, PA (US);

David Martin Dohan, San Francisco, CA (US);

Sagi Perel, Pittsburgh, PA (US);

Joao Ferdinando Gomes De Freitas, London, GB;

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/0985 (2023.01); G06N 3/0455 (2023.01); G06N 3/09 (2023.01); G06N 3/092 (2023.01);
U.S. Cl.
CPC ...
G06N 3/0985 (2023.01); G06N 3/0455 (2023.01); G06N 3/09 (2023.01); G06N 3/092 (2023.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes receiving metadata for the training, generating a metadata sequence that represents the metadata, at each of a plurality of iterations: generating one or more trials that each specify a respective value for each of a set of hyperparameters, comprising, for each trial: generating an input sequence for the iteration that comprises (i) the metadata sequence and (ii) for any earlier trials, a respective sequence that represents the respective values for the hyperparameters specified by the earlier trial and a measure of performance for the trial, and processing an input sequence for the trial that comprises the input sequence for the iteration using a sequence generation neural network to generate an output sequence that represents respective values for the hyperparameters.


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