公开(公告)号：US20240202589A1

公开(公告)日：2024-06-20

申请号：US18415212

申请日：2024-01-17

Applicant: Google LLC

Inventor： Jiaxun Wu ,  Amir Hossein Hormati

IPC: G06N20/00 ,  G06F16/242 ,  G06F16/25 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06F16/2433 ,  G06F16/258 ,  G06N5/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. In some implementations, an instruction to create a model is obtained. A determination is made whether the instruction specifies a transform. In response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. The training data is accessed. In response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. A model is generated with the transformed training data. A representation of the transform and the statistics is stored as metadata for the model.

公开(公告)号：US20220366318A1

公开(公告)日：2022-11-17

申请号：US17663430

申请日：2022-05-15

Applicant: Google LLC

Inventor： Jiaxun Wu ,  Ye Zichaun ,  Mingge Deng ,  Amir Hormati

IPC: G06N20/20 ,  G06F16/242 ,  G06F16/27

Abstract: A method, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving, from a user device, a hyperparameter optimization request requesting optimization of one or more hyperparameters of a machine learning model. The operations include obtaining training data for training the machine learning model and determining a set of hyperparameter permutations of the one or more hyperparameters. For each respective hyperparameter permutation in the set of hyperparameter permutations, the operations include training a unique machine learning model using the training data and the respective hyperparameter permutation and determining a performance of the trained model. The operations include selecting, based on the performance of each of the trained unique machine learning models of the user device, one of the trained unique machine learning models. The operations include generating one or more predictions using the selected one of the trained unique machine learning models.

公开(公告)号：US11928559B2

公开(公告)日：2024-03-12

申请号：US16843419

申请日：2020-04-08

Applicant: Google LLC

Inventor： Jiaxun Wu ,  Amir H. Hormati

IPC: G06F16/00 ,  G06F16/242 ,  G06F16/25 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06N20/00 ,  G06F16/2433 ,  G06F16/258 ,  G06N5/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. In some implementations, an instruction to create a model is obtained. A determination is made whether the instruction specifies a transform. In response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. The training data is accessed. In response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. A model is generated with the transformed training data. A representation of the transform and the statistics is stored as metadata for the model.

公开(公告)号：US20200320436A1

公开(公告)日：2020-10-08

申请号：US16843419

申请日：2020-04-08

Applicant: Google LLC

Inventor： Jiaxun Wu ,  Amir H. Hormati

IPC: G06N20/00 ,  G06F16/242 ,  G06N5/04 ,  G06F16/25

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. In some implementations, an instruction to create a model is obtained. A determination is made whether the instruction specifies a transform. In response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. The training data is accessed. In response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. A model is generated with the transformed training data. A representation of the transform and the statistics is stored as metadata for the model.