公开(公告)号：US20240232575A1

公开(公告)日：2024-07-11

申请号：US18618100

申请日：2024-03-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Xingchen WAN ,  Binxin RU ,  Pedro ESPERANCA ,  Fabio Maria CARLUCCI ,  Zhenguo LI

IPC: G06N3/04

CPC classification number: G06N3/04

Abstract: A neural network obtaining method, a data processing method, and a related device are disclosed. The disclosed methods may be used in the field of automatic neural architecture search technologies in the field of artificial intelligence. An example method includes: obtaining first indication information, where the first indication information indicates a probability and/or a quantity of times that k neural network modules appear in a first neural architecture cell; generating the first neural architecture cell based on the first indication information, and generating a first neural network; obtaining a target score corresponding to the first indication information, where the target score indicates performance of the first neural network; and obtaining second indication information from a plurality of pieces of first indication information based on a plurality of target scores, and obtaining a target neural network corresponding to the second indication information.

公开(公告)号：US20240311651A1

公开(公告)日：2024-09-19

申请号：US18668637

申请日：2024-05-20

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Binxin RU ,  Xingchen WAN ,  Pedro ESPERANCA ,  Fabio Maria CARLUCCI ,  Zhenguo LI

IPC: G06N3/0985 ,  G06N3/04

CPC classification number: G06N3/0985 ,  G06N3/04

Abstract: Disclosed is a method for searching for a neural network architecture ensemble model. The method includes: obtaining a dataset, where the dataset includes a sample and an annotation in a classification task; performing search by using a distributional neural network architecture search algorithm, including: determining a hyperparameter of a neural network architecture distribution; sampling a valid neural network architecture from the architecture distribution defined by the hyperparameter; training and evaluating the neural network architecture on the dataset, to obtain a performance indicator; determining, based on the performance indicator, neural network architecture distributions that share the hyperparameter, to obtain a candidate pool of base learners; and determining a surrogate model; and predicting test performance of the base learner in the candidate pool by using the surrogate model, and determining that k diverse base learners that meet a task scenario requirement form an ensemble model.