公开(公告)号：US20240330707A1

公开(公告)日：2024-10-03

申请号：US18623714

申请日：2024-04-01

Applicant: Douyin Vision Co., Ltd. ,  Lemon Inc.

Inventor： Anran XU ,  Shengjie WANG ,  Lei CHEN ,  Shuai LI ,  Jiankai SUN ,  Zhenzhe ZHENG ,  Fan WU

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: A method, apparatus, device, and medium for action execution are provided. In a method, a set of actions to be executed at a first device is determined from a plurality of actions based on a first action model at the first device. A data accumulated indicator associated with the set of actions is obtained, the data accumulated indicator indicating an amount of data to be sent from the first device to a second device associated with the first device. In response to that the data accumulated indicator meets a predetermined condition, parameter data associated with the set of actions is transmitted to the second device to cause the second device to update a second action model at the second device using the parameter data, the parameter data comprising reward data and consumption data associated with the set of actions respectively.

公开(公告)号：US20240311647A1

公开(公告)日：2024-09-19

申请号：US18604023

申请日：2024-03-13

Applicant: Douyin Vision Co., Ltd. ,  Lemon Inc.

Inventor： Xinwei WAN ,  Jiankai SUN ,  Shengjie WANG ,  Lei CHEN ,  Zhenzhe ZHENG ,  Fan WU ,  Guihai CHEN

IPC: G06N3/098 ,  G06N3/045

CPC classification number: G06N3/098 ,  G06N3/045

Abstract: A method according to embodiments of the present disclosure includes generating a multi-classification label set corresponding to an object set based on a binary classification label set corresponding to the object set. The method further includes receiving an embedding vector set from a non-label party model, wherein an embedding vector in the embedding vector set is generated based on a feature of an object in the object set. The method further includes generating a label party model based on the embedding vector set and the multi-classification label set, wherein the label party model includes a first network and a second network. The method according to embodiments of the present disclosure enables a label party to protect privacy of an original label set under the condition of joint training with a non-label party, and prevent the non-label party from inferring original labels corresponding to original features by various means.