公开(公告)号：US20210150366A1

公开(公告)日：2021-05-20

申请号：US16877333

申请日：2020-05-18

Applicant: salesforce.com, inc.

Inventor： Govardana Sachithanandam Ramachandran ,  Ka Chun Au ,  Shashank Harinath ,  Wenhao Liu ,  Alexis Roos ,  Caiming Xiong

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

Abstract: An embodiment proposed herein uses sparsification techniques to train the neural network with a high feature dimension that may yield desirable in-domain detection accuracy but may prune away dimensions in the output that are less important. Specifically, a sparsification vector is generated based on Gaussian distribution (or other probabilistic distribution) and is used to multiply with the higher dimension output to reduce the number of feature dimensions. The pruned output may be then used for the neural network to learn the sparsification vector. In this way, out-of-distribution detection accuracy can be improved.

公开(公告)号：US20210150340A1

公开(公告)日：2021-05-20

申请号：US16877339

申请日：2020-05-18

Applicant: salesforce.com, inc.

Inventor： Wenhao Liu ,  Ka Chun Au ,  Shashank Harinath ,  Bryan McCann ,  Govardana Sachithanandam Ramachandran ,  Alexis Roos ,  Caiming Xiong

IPC: G06N3/08 ,  G06F40/40 ,  G06N3/04

Abstract: Embodiments described herein provides a training mechanism that transfers the knowledge from a trained BERT model into a much smaller model to approximate the behavior of BERT. Specifically, the BERT model may be treated as a teacher model, and a much smaller student model may be trained using the same inputs to the teacher model and the output from the teacher model. In this way, the student model can be trained within a much shorter time than the BERT teacher model, but with comparable performance with BERT.

公开(公告)号：US20200153765A1

公开(公告)日：2020-05-14

申请号：US16262508

申请日：2019-01-30

Applicant: salesforce.com, inc.

Inventor： Noah William Burbank ,  Gabriel Starr Krupa ,  Bradford William Powley ,  Alexis Roos

IPC: H04L12/58 ,  G06F17/27

Abstract: A database server may identify mentioned names in a body of a message and extract the names using name identification heuristics and algorithms. The service retrieves or utilizes a distributed connection graph to identify contacts associated with the parties to the conversation that may match or be similar to the mentioned name. Contacts may be scored based on similarities between the extracted name the names associated with nodes of the graph, as well as other factors. The highest scoring contact may be surfaced or displayed to one or more of the parties to the communication message.