公开(公告)号：US20240152809A1

公开(公告)日：2024-05-09

申请号：US18412975

申请日：2024-01-15

Applicant: Google LLC

Inventor： Jyrki A. Alakuijala ,  Quentin Lascombes De Laroussilhe ,  Andrey Khorlin ,  Jeremiah Joseph Harmsen ,  Andrea Gesmundo

IPC: G06N20/00 ,  G06N3/08

CPC classification number: G06N20/00 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a machine learning model that is trained to perform a machine learning task. In one aspect, a method comprises receiving a request to train a machine learning model on a set of training examples; determining a set of one or more meta-data values characterizing the set of training examples; using a mapping function to map the set of meta-data values characterizing the set of training examples to data identifying a particular machine learning model architecture; selecting, using the particular machine learning model architecture, a final machine learning model architecture for performing the machine learning task; and training a machine learning model having the final machine learning model architecture on the set of training examples.

公开(公告)号：US11900222B1

公开(公告)日：2024-02-13

申请号：US16355185

申请日：2019-03-15

Applicant: Google LLC

Inventor： Jyrki A. Alakuijala ,  Quentin Lascombes de Laroussilhe ,  Andrey Khorlin ,  Jeremiah Joseph Harmsen ,  Andrea Gesmundo

IPC: G06N20/00 ,  G06N3/08

CPC classification number: G06N20/00 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a machine learning model that is trained to perform a machine learning task. In one aspect, a method comprises receiving a request to train a machine learning model on a set of training examples; determining a set of one or more meta-data values characterizing the set of training examples; using a mapping function to map the set of meta-data values characterizing the set of training examples to data identifying a particular machine learning model architecture; selecting, using the particular machine learning model architecture, a final machine learning model architecture for performing the machine learning task; and training a machine learning model having the final machine learning model architecture on the set of training examples.