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:
Oct. 24, 2023

Filed:

Oct. 05, 2022
Applicant:

Neumora Therapeutics, Inc., San Francisco, CA (US);

Inventors:

Tathagata Banerjee, Waltham, MA (US);

Matthew Edward Kollada, Deerfield, IL (US);

Assignee:

Neumora Therapeutics, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 40/20 (2018.01); G16H 50/70 (2018.01); G16H 50/20 (2018.01); G06N 3/02 (2006.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G16H 30/40 (2018.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01); G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G16H 40/20 (2018.01); G06N 3/02 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G06V 10/774 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G06N 20/00 (2019.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30104 (2013.01); G06V 2201/03 (2022.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an encoder neural network and a decoder neural network. In one aspect, a method comprises: updating current values of a set of encoder parameters and current values of a set of decoder parameters using gradients of a reconstruction loss function that measures an error in a reconstruction of multi-modal data from a training example, wherein: the reconstruction loss function comprises a plurality of scaling factors that each scale a respective term in the reconstruction loss function that measures an error in the reconstruction of a corresponding proper subset of feature dimensions of the multi-modal data from the training example.


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