公开(公告)号：US20200272940A1

公开(公告)日：2020-08-27

申请号：US16398757

申请日：2019-04-30

Applicant: salesforce.com, inc.

Inventor： Lichao Sun ,  Caiming XIONG ,  Jia LI ,  Richard SOCHER

IPC: G06N20/00 ,  G06K9/62 ,  G06N3/08 ,  G06F21/62

Abstract: Approaches for private and interpretable machine learning systems include a system for processing a query. The system includes one or more teacher modules for receiving a query and generating a respective output, one or more privacy sanitization modules for privacy sanitizing the respective output of each of the one or more teacher modules, and a student module for receiving a query and the privacy sanitized respective output of each of the one or more teacher modules and generating a result. Each of the one or more teacher modules is trained using a respective private data set. The student module is trained using a public data set. In some embodiments, human understandable interpretations of an output from the student module is provided to a model user.

公开(公告)号：US20210089882A1

公开(公告)日：2021-03-25

申请号：US16658399

申请日：2019-10-21

Applicant: salesforce.com, inc.

Inventor： Lichao SUN ,  Jia LI ,  Caiming XIONG ,  Yingbo ZHOU

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems and methods are provided for near-zero-cost (NZC) query framework or approach for differentially private deep learning. To protect the privacy of training data during learning, the near-zero-cost query framework transfers knowledge from an ensemble of teacher models trained on partitions of the data to a student model. Privacy guarantees may be understood intuitively and expressed rigorously in terms of differential privacy. Other features are also provided.

公开(公告)号：US20200372319A1

公开(公告)日：2020-11-26

申请号：US16559196

申请日：2019-09-03

Applicant: salesforce.com, inc.

Inventor： Lichao SUN ,  Kazuma HASHIMOTO ,  Jia LI ,  Richard SOCHER ,  Caiming XIONG

IPC: G06N3/00 ,  G06N3/04 ,  G06N3/08 ,  G06F17/27

Abstract: A method for evaluating robustness of one or more target neural network models using natural typos. The method includes receiving one or more natural typo generation rules associated with a first task associated with a first input document type, receiving a first target neural network model, and receiving a first document and corresponding its ground truth labels. The method further includes generating one or more natural typos for the first document based on the one or more natural typo generation rules, and providing, to the first target neural network model, a test document generated based on the first document and the one or more natural typos as an input document to generate a first output. A robustness evaluation result of the first target neural network model is generated based on a comparison between the output and the ground truth labels.

公开(公告)号：US20230252345A1

公开(公告)日：2023-08-10

申请号：US17827334

申请日：2022-05-27

Applicant: salesforce.com, inc.

Inventor： Yongjun Chen ,  Jia LI ,  Nitish Shirish Keskar ,  Caiming Xiong

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Embodiments described herein provide methods and systems for training a sequential recommendation model. A system receives a plurality of user behavior sequences, and encodes those sequences into a plurality of user interest representations. The system predicts a next item using a sequential recommendation model, producing a probability distribution over a set of items. The next interacted item in a sequence is selected as a positive sample, and a negative sample is selected based on the generated probability distribution. The positive and negative samples are used to compute a contrastive loss and update the sequential recommendation model.