公开(公告)号：US20220405579A1

公开(公告)日：2022-12-22

申请号：US17613773

申请日：2021-03-03

Applicant: Google LLC

Inventor： Jiahui Yu ,  Pengchong Jin ,  Hanxiao Liu ,  Gabriel Mintzer Bender ,  Pieter-Jan Kindermans ,  Mingxing Tan ,  Xiaodan Song ,  Ruoming Pang ,  Quoc V. Le

IPC: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.

公开(公告)号：US11531861B2

公开(公告)日：2022-12-20

申请号：US16258927

申请日：2019-01-28

Applicant: Google LLC

Inventor： Mingxing Tan ,  Quoc Le ,  Bo Chen ,  Vijay Vasudevan ,  Ruoming Pang

IPC: G06N3/04 ,  G06N20/10 ,  G06F17/15 ,  G06N3/08

Abstract: The present disclosure is directed to an automated neural architecture search approach for designing new neural network architectures such as, for example, resource-constrained mobile CNN models. In particular, the present disclosure provides systems and methods to perform neural architecture search using a novel factorized hierarchical search space that permits layer diversity throughout the network, thereby striking the right balance between flexibility and search space size. The resulting neural architectures are able to be run relatively faster and using relatively fewer computing resources (e.g., less processing power, less memory usage, less power consumption, etc.), all while remaining competitive with or even exceeding the performance (e.g., accuracy) of current state-of-the-art mobile-optimized models.

公开(公告)号：US11450096B2

公开(公告)日：2022-09-20

申请号：US17564860

申请日：2021-12-29

Applicant: Google LLC

Inventor： Mingxing Tan ,  Quoc V. Le

IPC: G06V10/00 ,  G06V10/774 ,  G06V10/776

Abstract: Systems and methods of the present disclosure can include a computer-implemented method for efficient machine-learned model training. The method can include obtaining a plurality of training samples for a machine-learned model. The method can include, for one or more first training iterations, training, based at least in part on a first regularization magnitude configured to control a relative effect of one or more regularization techniques, the machine-learned model using one or more respective first training samples of the plurality of training samples. The method can include, for one or more second training iterations, training, based at least in part on a second regularization magnitude greater than the first regularization magnitude, the machine-learned model using one or more respective second training samples of the plurality of training samples.

公开(公告)号：US20220019869A1

公开(公告)日：2022-01-20

申请号：US17039178

申请日：2020-09-30

Applicant: Google LLC

Inventor： Sheng Li ,  Norman Paul Jouppi ,  Quoc V. Le ,  Mingxing Tan ,  Ruoming Pang ,  Liqun Cheng ,  Andrew Li

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining an architecture for a task neural network that is configured to perform a particular machine learning task on a target set of hardware resources. When deployed on a target set of hardware, such as a collection of datacenter accelerators, the task neural network may be capable of performing the particular machine learning task with enhanced accuracy and speed.