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：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.