公开(公告)号：US20210365767A1

公开(公告)日：2021-11-25

申请号：US17398750

申请日：2021-08-10

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Liujie ZHANG ,  Xiang LAN ,  Tao LUO ,  Huihuang ZHENG

IPC: G06N3/063 ,  G06F9/30 ,  G06F9/445

Abstract: The disclosure provides a method and a device for operator registration processing based on deep learning, an electronic device, and a storage medium. Description information and logic operation information of a registered operator are obtained. An operator type of the registered operator is determined based on the logic operation information. Configuration information corresponding to the operator type and preset in a deep learning framework is obtained. It is detected whether the registered operator is registered completely based on the description information and the configuration information.

公开(公告)号：US20210326248A1

公开(公告)日：2021-10-21

申请号：US17362658

申请日：2021-06-29

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Chunle ZHANG ,  Tao LUO ,  Xiang LAN

IPC: G06F11/36 ,  G06F11/32 ,  G06F11/07

Abstract: A method, apparatus, device and storage medium for testing a code for a software development project are provided. The method may include: determining, in response to receiving a code submission request of a user, a code submitted by the user; performing integration testing on the code, and performing at least one preset test task; acquiring error information of the at least one preset test task; generating, in response to determining that the error information meets a preset condition, warning information based on the error information; and outputting the warning information.

公开(公告)号：US20210406641A1

公开(公告)日：2021-12-30

申请号：US17248131

申请日：2021-01-11

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Huihuang ZHENG ,  Xiang LAN ,  Yamei LI ,  Liujie ZHANG ,  Fei GUO ,  Yanjun MA ,  Dianhai YU

IPC: G06N3/04

Abstract: A data processing method and apparatus based on a recurrent neural network and a device are provided. The recurrent neural network includes multiple recurrent units, each recurrent unit includes multiple data processing nodes and a start node, at least one recurrent unit includes an end node, and at least one data processing node is included between the start node and the end node. During the processing of the first target processing object in a first recurrent unit, in a case that the first target processing object does not satisfy the first preset condition, the start node in the first recurrent unit is run to add a tag to the data processing nodes subsequent to the start node and stop addition of the tag in response to reaching the end node, and no processing is performed by the data processing nodes with the tag.