公开(公告)号：US20240242089A1

公开(公告)日：2024-07-18

申请号：US18565015

申请日：2022-04-28

Applicant: Lemon Inc.

Inventor： Jiankai SUN ,  Weihao GAO ,  Junyuan XIE ,  Chong WANG

IPC: G06N3/098 ,  G06N3/04

CPC classification number: G06N3/098 ,  G06N3/04

Abstract: The present disclosure relates to a data protection method, a training method and apparatus for a network structure, a medium, and a device. The data protection method includes: obtaining original feature information of a target batch of reference samples for a passive party of a joint training model; and processing the original feature information by means of a target feature processing network structure to obtain target feature information corresponding to the original feature information. A neural network structure is trained by at least aiming at minimizing a coupling degree of between original training feature information and target training feature information of training samples for the passive party to obtain the target feature processing network structure. The target training feature information is feature information corresponding to the original training feature information that is outputted from the neural network structure using the original training feature information as an input.

公开(公告)号：US20230267363A1

公开(公告)日：2023-08-24

申请号：US17666076

申请日：2022-02-07

Applicant: Lemon Inc.

Inventor： Yingxiang YANG ,  Tianyi LIU ,  Taiqing WANG ,  Chong WANG ,  Zhihan XIONG

IPC: G06N20/00 ,  G06F17/14

CPC classification number: G06N20/00 ,  G06F17/14

Abstract: Embodiments of the present disclosure relate to machine learning with periodic data. According to embodiments of the present disclosure, a feature representation of an input data sample is obtained from a prediction model. First Fourier coefficients for a first component in a Fourier expansion are determined by applying the feature representation into a first mapping model, and second Fourier coefficients for a second component in the Fourier expansion are determined by applying the feature representation into a second mapping model. A Fourier expansion result is determined based on the first Fourier coefficients and the second Fourier coefficients in the Fourier expansion, and a prediction result for the input data sample is determined based on the Fourier expansion result.