公开(公告)号：US20240211759A1

公开(公告)日：2024-06-27

申请号：US18596535

申请日：2024-03-05

Applicant: Google LLC

Inventor： Gregory S. Corrado ,  Kai Chen ,  Jeffrey A. Dean ,  Gary R. Holt ,  Julian P. Grady ,  Sharat Chikkerur ,  David W. Sculley, II

IPC: G06N3/08 ,  G06F7/483 ,  G06F17/16 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084

CPC classification number: G06N3/08 ,  G06F7/483 ,  G06F17/16 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084 ,  G06F2207/483

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

公开(公告)号：US10679124B1

公开(公告)日：2020-06-09

申请号：US15368460

申请日：2016-12-02

Applicant: Google LLC

Inventor： Gregory S. Corrado ,  Kai Chen ,  Jeffrey A. Dean ,  Gary R. Holt ,  Julian P. Grady ,  Sharat Chikkerur ,  David W. Sculley, II

IPC: G06N3/08 ,  G06N3/04 ,  G06F7/483 ,  G06F17/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

公开(公告)号：US20190080019A1

公开(公告)日：2019-03-14

申请号：US16129154

申请日：2018-09-12

Applicant: Google LLC

Inventor： Andrew Richard Young ,  Jonathan Joseph Conley ,  Gary R. Holt

IPC: G06F17/30 ,  G06F15/18 ,  G06N3/08 ,  G06K9/62

Abstract: Systems, methods, devices, and techniques for improving the efficiency of selecting digital components to present in electronic documents and reducing latency in rendering digital components in electronic documents. In some implementations, a content distribution system uses predicted metrics for a set of candidate components to determine items to present in an electronic document responsive to a request. A metric prediction model can generate predicted metrics with the aid of a parameter prediction sub-model that predicts a value of a non-observable parameter associated with a request for a digital component.

公开(公告)号：US20230325657A1

公开(公告)日：2023-10-12

申请号：US17972466

申请日：2022-10-24

Applicant: Google LLC

Inventor： Gregory S. Corrado ,  Kai Chen ,  Jeffrey A. Dean ,  Gary R. Holt ,  Julian P. Grady ,  Sharat Chikkerur ,  David W. Sculley, II

IPC: G06N3/08 ,  G06N3/084 ,  G06F7/483 ,  G06F17/16 ,  G06N3/045 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/084 ,  G06F7/483 ,  G06F2207/483 ,  G06N3/045 ,  G06N3/04 ,  G06F17/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

公开(公告)号：US11954597B2

公开(公告)日：2024-04-09

申请号：US17972466

申请日：2022-10-24

Applicant: Google LLC

Inventor： Gregory S. Corrado ,  Kai Chen ,  Jeffrey A. Dean ,  Gary R. Holt ,  Julian P. Grady ,  Sharat Chikkerur ,  David W. Sculley, II

IPC: G06N3/08 ,  G06F7/483 ,  G06F17/16 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084

CPC classification number: G06N3/08 ,  G06F7/483 ,  G06F17/16 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084 ,  G06F2207/483

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

公开(公告)号：US11481631B1

公开(公告)日：2022-10-25

申请号：US16895855

申请日：2020-06-08

Applicant: Google LLC

Inventor： Gregory S. Corrado ,  Kai Chen ,  Jeffrey A. Dean ,  Gary R. Holt ,  Julian P Grady ,  Sharat Chikkerur ,  David W. Sculley, II

IPC: G06N3/08 ,  G06N3/04 ,  G06F17/16 ,  G06F7/483

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.