公开(公告)号：US11665100B2

公开(公告)日：2023-05-30

申请号：US16894425

申请日：2020-06-05

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Ke He ,  Zhitang Chen ,  Yunfeng Shao

IPC: G06T7/00 ,  H04L47/2483 ,  G06T7/33 ,  H04L69/22 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V20/40

CPC classification number: H04L47/2483 ,  G06N3/045 ,  G06T7/33 ,  G06V10/764 ,  G06V10/82 ,  G06V10/95 ,  G06V20/46 ,  H04L69/22

Abstract: This application provides a data stream identification method and apparatus and belongs to the field of Internet technologies. The method includes: obtaining packet transmission attribute information of N consecutive packets in a target data stream; generating feature images of the packet transmission attribute information of the N consecutive packets based on the packet transmission attribute information of the N consecutive packets; and inputting the feature images into a pre-trained image classification model, to obtain a target application identifier corresponding to the target data stream. According to this application, accuracy of identifying an application identifier corresponding to a data stream can be improved.

公开(公告)号：US11386350B2

公开(公告)日：2022-07-12

申请号：US15980866

申请日：2018-05-16

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yunfeng Shao ,  Jun Xu ,  Masood Mortazavi

IPC: G06N20/00 ,  G06F30/00 ,  G06N3/08 ,  G06N3/04 ,  G06F3/06 ,  H04L41/16

Abstract: The method and apparatus that are applied to a machine learning system which includes at least one parameter collection group and at least one parameter delivery group. Each parameter collection group is corresponding to at least one parameter delivery group. The method includes: when any parameter collection group meets an intra-group combination condition, combining model parameters of M nodes in the parameter collection group to obtain a first model parameter of the parameter collection group, where a smallest quantity s of combination nodes in the parameter collection group≤M≤a total quantity of nodes included in the parameter collection group; and sending the first model parameter of the parameter collection group to N nodes in a parameter delivery group corresponding to the parameter collection group, where 1≤N≤a total quantity of nodes included in the parameter delivery group corresponding to the parameter collection group.

公开(公告)号：US20180267927A1

公开(公告)日：2018-09-20

申请号：US15980496

申请日：2018-05-15

Applicant: HUAWEI TECHNOLOGIES CO.,LTD.

Inventor： Jun Xu ,  Yunfeng Shao ,  Xiao Yang

IPC: G06F15/18 ,  G06K9/62

CPC classification number: G06N20/00 ,  G06F16/00 ,  G06K9/6288

Abstract: Embodiments of the present invention provide a model, which relate to the field of machine learning and intend to reduce a data transmission amount and implement dynamical adjustment of computing resources during model parameter fusion. The method includes: dividing, by an ith node, a model parameter of the ith node into N blocks, where the ith node is any node of N nodes that participate in a fusion, and 1≤i≤N≤M; receiving, by the ith node, ith model parameter blocks respectively sent by other nodes of the N nodes than the ith node; fusing, by the ith node, an ith model parameter block of the ith node and the ith model parameter blocks respectively sent by the other nodes, so as to obtain the ith general model parameter block; and distributing, by the ith node, the ith general model parameter block to the other nodes of the N nodes.