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. 01, 2023

Filed:

May. 19, 2017
Applicant:

Deepmind Technologies Limited, London, GB;

Inventors:

Oriol Vinyals, London, GB;

Alexander Benjamin Graves, London, GB;

Wojciech Czarnecki, London, GB;

Koray Kavukcuoglu, London, GB;

Simon Osindero, London, GB;

Maxwell Elliot Jaderberg, London, GB;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/084 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network including a first subnetwork followed by a second subnetwork on training inputs by optimizing an objective function. In one aspect, a method includes processing a training input using the neural network to generate a training model output, including processing a subnetwork input for the training input using the first subnetwork to generate a subnetwork activation for the training input in accordance with current values of parameters of the first subnetwork, and providing the subnetwork activation as input to the second subnetwork; determining a synthetic gradient of the objective function for the first subnetwork by processing the subnetwork activation using a synthetic gradient model in accordance with current values of parameters of the synthetic gradient model; and updating the current values of the parameters of the first subnetwork using the synthetic gradient.


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