公开(公告)号：US20230342589A1

公开(公告)日：2023-10-26

申请号：US17728398

申请日：2022-04-25

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Julia Renee Watson ,  Garrett Raymond Honke ,  Estefany Kelly Buchanan ,  Hailey Anne Trier ,  Grayr Bleyan ,  Blair Armstrong ,  Rebecca Dawn Finzi

IPC: G06N3/04 ,  G06N20/20 ,  G06K9/62

CPC classification number: G06N3/0454 ,  G06N20/20 ,  G06K9/6215 ,  G06K9/6227 ,  G06K9/6262

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for executing ensemble models that include multiple reservoir computing neural networks. One of the methods includes executing an ensemble model comprising a plurality of reservoir computing neural networks, the ensemble model having been trained by operations comprising, at each training stage in a sequence of training stages: obtaining a current ensemble model that comprises a plurality of current reservoir computing neural networks; determining a respective performance measure for each current reservoir computing neural network in the current ensemble model; determining one or more new reservoir computing neural networks to be added to the current ensemble model based on the performance measures for the current reservoir computing neural networks; and adding the new reservoir computing neural networks to the current ensemble model.

公开(公告)号：US20230142885A1

公开(公告)日：2023-05-11

申请号：US17524574

申请日：2021-11-11

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Hailey Anne Trier

IPC: G06N3/06 ,  G06N3/04 ,  G06F16/901

CPC classification number: G06N3/061 ,  G06N3/04 ,  G06F16/9024

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus, the method including: obtaining data defining a connectivity graph that represents synaptic connectivity between multiple biological neuronal elements in a brain of a biological organism, where the connectivity graph includes: multiple nodes, and multiple edges that each connect a respective pair of nodes, determining a partition of the connectivity graph into multiple community sub-graphs by performing an optimization that encourages a higher measure of connectedness between nodes included within each community sub-graph relative to nodes included in different community sub-graphs, and selecting a neural network architecture for performing a machine learning task using multiple community sub-graphs determined by the optimization that encourages the higher measure of connectedness between nodes included within each community sub-graph relative to nodes included in different community sub-graphs.