公开(公告)号：US20230042327A1

公开(公告)日：2023-02-09

申请号：US17579377

申请日：2022-01-19

Applicant: salesforce.com, inc.

Inventor： Zhiwei Liu ,  Caiming Xiong ,  Jia Li ,  Yongjun Chen

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for providing a neural network system includes performing contrastive learning to the neural network system to generate a trained neural network system. The performing the contrastive learning includes performing first model augmentation to a first encoder of the neural network system to generate a first embedding of a sample, performing second model augmentation to the first encoder to generate a second embedding of the sample, and optimizing the first encoder using a contrastive loss based on the first embedding and the second embedding. The trained neural network system is provided to perform a task.

公开(公告)号：US20230073754A1

公开(公告)日：2023-03-09

申请号：US17586451

申请日：2022-01-27

Applicant: salesforce.com, inc.

Inventor： Yongjun Chen ,  Zhiwei Liu ,  Jia Li ,  Caiming Xiong

IPC: G06K9/62

Abstract: Embodiments described herein provides an intent prototypical contrastive learning framework that leverages intent similarities between users with different behavior sequences. Specifically, user behavior sequences are encoded into a plurality of user interest representations. The user interest representations are clustered into a plurality of clusters based on mutual distances among the user interest representations in a representation space. Intention prototypes are determined based on centroids of the clusters. A set of augmented views for user behavior sequences are created and encoded into a set of view representations. A contrastive loss is determined based on the set of augmented views and the plurality of intention prototypes. Model parameters are updated based at least in part on the contrastive loss.