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：US20250037806A1

公开(公告)日：2025-01-30

申请号：US18717362

申请日：2023-04-20

Applicant: Beijing Bytedance Network Technology Co., Ltd. ,  Lemon Inc.

Inventor： Xiang GAO ,  Weihao GAO ,  Wenzhi XIAO ,  Zhirui WANG ,  Liang XIANG ,  Chong WANG

IPC: G16C20/70

Abstract: According to implementations of the present disclosure, a method, apparatus, device and medium for managing molecular prediction is provided. In the method, an upstream model is obtained from a portion of network layers in a pretrained model, the pretrained model describing an association between a molecular structure and molecular energy. A downstream model is determined based on a molecular prediction purpose, and an output layer of the downstream model is determined based on the molecular prediction purpose. A molecular prediction model is generated based on the upstream model and the downstream model, the molecular prediction model describing an association between a molecular structure and a molecular prediction purpose associated with the molecular structure. Since the upstream model may have extensive knowledge related to molecules, the amount of training data required to train the molecular prediction model that is generated based on the upstream model and the downstream model may be reduced.

公开(公告)号：US20240249004A1

公开(公告)日：2024-07-25

申请号：US18565000

申请日：2022-04-28

Applicant: Lemon Inc.

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

IPC: G06F21/60 ,  G06N20/20

CPC classification number: G06F21/602 ,  G06N20/20

Abstract: The present disclosure relates to a method and a device for data protection, a readable medium and an electronic apparatus, and the method comprises: acquiring a target identification information union set, wherein the target identification information union set comprises target encryption identification information of a first party of a joint training model and target encryption identification information of a second party of the joint training model, the target encryption identification information in the target identification information union set being obtained by encrypting according to a secret key of the first party and a secret key of the second party; and determining, according to the target identification information union set, a target sample data set for training the joint training model. Therefore, an identification information intersection of the first party and the second party does not need to be determined in advance as in the related technology.

公开(公告)号：US20240220641A1

公开(公告)日：2024-07-04

申请号：US18565962

申请日：2022-07-15

Applicant: Lemon Inc.

Inventor： Jiankai SUN ,  Xin YANG ,  Aonan ZHANG ,  Weihao GAO ,  Junyuan XIE ,  Chong WANG

IPC: G06F21/60 ,  G06N20/00

CPC classification number: G06F21/602 ,  G06N20/00

Abstract: The present disclosure relates to a data protection method, apparatus, medium and electronic device. The method comprises: obtaining a specified batch of reference samples of an active participant of a joint training model; determining generation gradient information of the first reference sample; determining target gradient information sent to the passive participant according to the generation gradient information, and sending the target gradient information to the passive participant, to update, by the passive participant, parameters of the joint training model according to the target gradient information. Through the above solution, the influence of the generated data on the training process and model performance of the joint training model is avoided as much as possible, and the privacy and security of data are improved.

公开(公告)号：US20250125020A1

公开(公告)日：2025-04-17

申请号：US18896056

申请日：2024-09-25

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

Inventor： Weihao GAO ,  Sheng GONG ,  Zhenliang MU ,  Yumin ZHANG

IPC: G16C20/70 ,  G16C20/30 ,  G16C60/00

Abstract: The embodiment of the invention provides method, apparatus, device and a storage medium for training and optimizing an analysis model. The method of optimizing the analysis model includes: fine-tuning an analysis model with a first set of values regarding a first property of a target material to determine a second set of values regarding a second property of the target material; determining an association between the first property and the second property of the target material based on a first set of values and a second set of values; determining a target value of the target material regarding the first property with the association based on a reference value of the target material regarding the second property, the reference value being determined based on an experiment on target material; and optimizing the analysis model with the target value of the target material regarding the first property. In this way, embodiments of the present disclosure can utilize limited experimental data to optimize the analysis model.

公开(公告)号：US20250044783A1

公开(公告)日：2025-02-06

申请号：US18713988

申请日：2022-11-07

Applicant: Lemon Inc.

Inventor： Weihao GAO ,  Ce YANG ,  Di WU ,  Chong WANG

IPC: G05B23/02

Abstract: According to embodiments of the present disclosure, there are provided a method, device, medium, and product for state prediction. The method includes: obtaining a neural network, the neural network being trained to determine a state change of a physical system over time, training data of the neural network indicating states of a plurality of physical systems at a plurality of times; obtaining state data corresponding to a state of a target physical system at a first time; determining respective unit feature representations of the physical units in the target physical system based at least on target values of material properties of the physical units; and determining a state of the target physical system at a second time based on the state data by inputting at least the unit feature representations to the neural network. Through the above solution, generalization capability of the neural network can be significantly improved.

公开(公告)号：US20240005210A1

公开(公告)日：2024-01-04

申请号：US18252559

申请日：2021-11-06

Applicant: Lemon Inc.

Inventor： Jiankai SUN ,  Weihao GAO ,  Chong WANG ,  Hongyi ZHANG ,  Xiaobing LIU ,  Runliang LI ,  Xin YANG

IPC: G06N20/00 ,  G06F21/60

CPC classification number: G06N20/00 ,  G06F21/60

Abstract: The present disclosure relates to a data protection method, an apparatus, a medium and a device. The method includes: acquiring gradient association information respectively corresponding to reference samples of a target batch of an active party of a joint training model; according to the proportion occupied respectively by reference samples of positive examples and reference samples of negative examples in all reference samples of the target batch, determining a constraint condition of the data noise to be added; determining information of said data noise according to the gradient association information and the constraint condition corresponding to the reference samples; correcting, according to the information of said data noise, an initial gradient transmission value corresponding to each reference sample, so as to obtain target gradient transmission information; and sending the target gradient transmission information to a passive party of the joint training model.