公开(公告)号：US20240370717A1

公开(公告)日：2024-11-07

申请号：US18313189

申请日：2023-05-05

Applicant: Google LLC

Inventor： Qifei Wang ,  Yicheng Fan ,  Wei Xu ,  Jiayu Ye ,  Lu Wang ,  Chuo-Ling Chang ,  Dana Alon ,  Erik Nathan Vee ,  Hongkun Yu ,  Matthias Grundmann ,  Shanmugasundaram Ravikumar ,  Andrew Stephen Tomkins

IPC: G06N3/08

Abstract: A method for a cross-platform distillation framework includes obtaining a plurality of training samples. The method includes generating, using a student neural network model executing on a first processing unit, a first output based on a first training sample. The method also includes generating, using a teacher neural network model executing on a second processing unit, a second output based on the first training sample. The method includes determining, based on the first output and the second output, a first loss. The method further includes adjusting, based on the first loss, one or more parameters of the student neural network model. The method includes repeating the above steps for each training sample of the plurality of training samples.

公开(公告)号：US20220374719A1

公开(公告)日：2022-11-24

申请号：US17861930

申请日：2022-07-11

Applicant: Google LLC

Inventor： Sujith Ravi ,  Gaurav Menghani ,  Prabhu Kaliamoorthi ,  Yicheng Fan

IPC: G06N3/08 ,  G06F8/76 ,  G06F9/54 ,  G06K9/62

Abstract: The present disclosure provides an application development platform and associated software development kits (“SDKs”) that provide comprehensive services for generation, deployment, and management of machine-learned models used by computer applications such as, for example, mobile applications executed by a mobile computing device. In particular, the application development platform and SDKs can provide or otherwise leverage a unified, cross-platform application programming interface (“API”) that enables access to all of the different machine learning services needed for full machine learning functionality within the application. In such fashion, developers can have access to a single SDK for all machine learning services.

公开(公告)号：US20240232686A1

公开(公告)日：2024-07-11

申请号：US18012292

申请日：2022-07-29

Applicant: Google LLC

Inventor： Yicheng Fan ,  Jingyue Shen ,  Deqiang Chen ,  Peter Shaosen Young ,  Dana Alon ,  Erik Nathan Vee ,  Shanmugasundaram Ravikumar ,  Andrew Tomkins

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems and methods of the present disclosure are directed to portion-specific compression and optimization of machine-learned models. For example, a method for portion-specific compression and optimization of machine-learned models includes obtaining data descriptive of one or more respective sets of compression schemes for one or more model portions of a plurality of model portions of a machine-learned model. The method includes evaluating a cost function to respectively select one or more candidate compression schemes from the one or more sets of compression schemes. The method includes respectively applying the one or more candidate compression schemes to the one or more model portions to obtain a compressed machine-learned model comprising one or more compressed model portions that correspond to the one or more model portions.

公开(公告)号：US11410044B2

公开(公告)日：2022-08-09

申请号：US16605702

申请日：2018-05-21

Applicant: Google LLC

Inventor： Sujith Ravi ,  Gaurav Menghani ,  Prabhu Kaliamoorthi ,  Yicheng Fan

IPC: G06N3/08 ,  G06F8/76 ,  G06F9/54 ,  G06K9/62

Abstract: The present disclosure provides an application development platform and associated software development kits (“SDKs”) that provide comprehensive services for generation, deployment, and management of machine-learned models used by computer applications such as, for example, mobile applications executed by a mobile computing device. In particular, the application development platform and SDKs can provide or otherwise leverage a unified, cross-platform application programming interface (“API”) that enables access to all of the different machine learning services needed for full machine learning functionality within the application. In such fashion, developers can have access to a single SDK for all machine learning services.