公开(公告)号：US20230143789A1

公开(公告)日：2023-05-11

申请号：US18149462

申请日：2023-01-03

Applicant: Lemon Inc.

Inventor： Shangyu Xie ,  Jiankai Sun ,  Xin Yang ,  Yuanshun Yao ,  Tianyi Liu ,  Taiqing Wang

IPC: G06N3/084 ,  G06N3/045

CPC classification number: G06N3/084 ,  G06N3/045

Abstract: Split learning is provided to train a composite neural network (CNN) model that is split into first and second submodels, including receiving a noise-laden backpropagation gradient, training the surrogate submodel by optimizing a gradient distance loss, and computing an updated dummy label using the first submodel and the trained surrogate submodel to infer label information of the second submodel. Noise can be added to a label of the second submodel or a shared backpropagation gradient to protect the label information.

公开(公告)号：US20230098656A1

公开(公告)日：2023-03-30

申请号：US18070461

申请日：2022-11-28

Applicant: Lemon Inc.

Inventor： Aonan Zhang ,  Jiankai Sun ,  Ruocheng Guo ,  Taiqing Wang ,  Xiaohui Chen

IPC: G06N20/20

Abstract: The present disclosure describes techniques for improving data subsampling for recommendation systems. A user-item graph associated with training data may be constructed. An importance of user-item interactions may be estimated via graph conductance based on the user-item graph. An importance of the training data may be measured via sample hardness using a pre-trained pilot model. A subsampling rate may be generated based on the importance estimated from the user-item graph and the importance measured by the pre-trained pilot model.