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    DISTRIBUTED MACHINE LEARNING
    2.
    发明申请
    DISTRIBUTED MACHINE LEARNING 审中-公开

    公开(公告)号：US20180240011A1

    公开(公告)日：2018-08-23

    申请号：US15439072

    申请日：2017-02-22

    Applicant: Cisco Technology, Inc.

    Inventor： Wai-tian Tan ,  Rob Liston ,  John G. Apostolopoulos ,  Xiaoqing Zhu

    IPC: G06N3/08 ,  G06F17/18

    CPC classification number: G06F17/18 ,  G06F9/46 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/084

    Abstract: Presented herein are techniques for training a central/global machine learning model in a distributed machine learning system. In the data sampling techniques, a subset of the data obtained at the local sites is intelligently selected for transfer to the central site for use in training the central machine learning model. In the model merging techniques, distributed local training occurs in each local site and copies of the local machine learning models are sent to the central site for aggregation of learning by merging of the models. As a result, in accordance with the examples presented herein, a central machine learning model can be trained based on various representations/transformations of data seen at the local machine learning models, including sampled selections of data-label pairs, intermediate representation of training errors, or synthetic data-label pairs generated by models trained at various local sites.

    COMMUNICATION EFFICIENT MACHINE LEARNING OF DATA ACROSS MULTIPLE SITES
    4.
    发明申请
    COMMUNICATION EFFICIENT MACHINE LEARNING OF DATA ACROSS MULTIPLE SITES 审中-公开

    公开(公告)号：US20200090002A1

    公开(公告)日：2020-03-19

    申请号：US16131150

    申请日：2018-09-14

    Applicant: Cisco Technology, Inc.

    Inventor： Xiaoqing Zhu ,  Yaqi Wang ,  Dan Tan ,  Rob Liston ,  Mehdi Nikkhah

    IPC: G06K9/62 ,  G06F15/18 ,  G06N3/04 ,  H04Q9/00

    Abstract: In one embodiment, a service receives machine learning-based generative models from a plurality of distributed sites. Each generative model is trained locally at a site using unlabeled data observed at that site to generate synthetic unlabeled data that mimics the unlabeled data used to train the generative model. The service receives, from each of the distributed sites, a subset of labeled data observed at that site. The service uses the generative models to generate synthetic unlabeled data. The service trains a global machine learning-based model using the received subsets of labeled data received from the distributed sites and the synthetic unlabeled data generated by the generative models.

    Clustering-based person re-identification
    5.
    发明授权
    Clustering-based person re-identification 有权

    公开(公告)号：US10275683B2

    公开(公告)日：2019-04-30

    申请号：US15409821

    申请日：2017-01-19

    Applicant: Cisco Technology, Inc.

    Inventor： Xiaoqing Zhu ,  Rob Liston ,  John G. Apostolopoulos ,  Wai-tian Tan

    IPC: G06K9/00 ,  G06K9/62 ,  G06F17/30

    Abstract: Presented herein are techniques for assignment of an identity to a group of captured images. A plurality of captured images that each include an image of at least one person are obtained. For each of the plurality of captured images, relational metrics indicating a relationship between the image of the person in a respective captured image and the images of the persons in each of the remaining plurality of captured images is calculated. Based on the relational metrics, a clustering process is performed to generate one or more clusters from the plurality of captured images. Each of the one or more clusters are associated with an identity of an identity database. The one or more clusters may each be associated with an existing identity of the identity database or an additional identity that is not yet present in the identity database.

    Training distributed machine learning with selective data transfers
    6.
    发明授权
    Training distributed machine learning with selective data transfers 有权

    公开(公告)号：US11144616B2

    公开(公告)日：2021-10-12

    申请号：US15439072

    申请日：2017-02-22

    Applicant: Cisco Technology, Inc.

    Inventor： Wai-tian Tan ,  Rob Liston ,  John G. Apostolopoulos ,  Xiaoqing Zhu

    IPC: G06F17/18 ,  G06F9/46 ,  G06N3/04 ,  G06N3/08 ,  G06N3/063 ,  G06N5/00

    Abstract: Presented herein are techniques for training a central/global machine learning model in a distributed machine learning system. In the data sampling techniques, a subset of the data obtained at the local sites is intelligently selected for transfer to the central site for use in training the central machine learning model. In the model merging techniques, distributed local training occurs in each local site and copies of the local machine learning models are sent to the central site for aggregation of learning by merging of the models. As a result, in accordance with the examples presented herein, a central machine learning model can be trained based on various representations/transformations of data seen at the local machine learning models, including sampled selections of data-label pairs, intermediate representation of training errors, or synthetic data-label pairs generated by models trained at various local sites.

    Automated generation of pre-labeled training data
    7.
    发明授权
    Automated generation of pre-labeled training data 有权

    公开(公告)号：US10242449B2

    公开(公告)日：2019-03-26

    申请号：US15397987

    申请日：2017-01-04

    Applicant: Cisco Technology, Inc.

    Inventor： Rob Liston ,  John G. Apostolopoulos

    IPC: G06T7/11

    Abstract: Presented herein are techniques for automatically generating object segmentation training data. In particular, a segmentation data generation system is configured to obtain training images derived from a scene captured by one or more image capture devices. Each training image is a still image that includes a foreground object and a background. The segmentation data generation system automatically generates a mask of the training image to delineate the object from the background and, based on the mask automatically generates a masked image. The masked image includes only the object present in the training image. The segmentation data generation system composites the masked image with an image of an environmental scene to generate a composite image that includes the masked image and the environmental scene.

    Adaptive localization and incremental deployment of infrastructure with crowd-sourced feedback
    8.
    发明授权
    Adaptive localization and incremental deployment of infrastructure with crowd-sourced feedback 有权

    公开(公告)号：US10145962B1

    公开(公告)日：2018-12-04

    申请号：US15726553

    申请日：2017-10-06

    Applicant: Cisco Technology, Inc.

    Inventor： Mehdi Nikkhah ,  Rob Liston ,  Wai-tan Tan ,  Xiaoqing Zhu ,  Santosh G. Pandey

    IPC: H04W24/00 ,  G01S19/46 ,  G01S19/48 ,  G01S19/25 ,  G01S19/06 ,  G01S19/47

    Abstract: A methodology includes receiving from a first mobile device a first estimated location of the first mobile device and a first estimated error associated with the first estimated location, the first estimated location being based on first coarse data from a first wireless access point location determination system fused with inertial measurement unit (IMU) data from the first mobile device, receiving from a second mobile device a second estimated location of the second mobile device and a second estimated error associated with the second estimated location, the second estimated location being based on second coarse data from the first wireless access point location determination system fused with IMU data from the second mobile device, and based on the first estimated error and the second estimated error, determining a recommended change to a deployment of a wireless access point associated with the first wireless access point location determination system.

    Power-over-Ethernet (POE)-Enabled Network Device and USB Device Power Negotiation using USB to POE Protocol Conversion
    10.
    发明申请
    Power-over-Ethernet (POE)-Enabled Network Device and USB Device Power Negotiation using USB to POE Protocol Conversion 有权
    以太网供电（POE）启用网络设备和USB设备电源协商使用USB到POE协议转换

    公开(公告)号：US20150331464A1

    公开(公告)日：2015-11-19

    申请号：US14558131

    申请日：2014-12-02

    Applicant: Cisco Technology, Inc.

    Inventor： Koussalya Balasubramanian ,  Rob Liston

    IPC: G06F1/26 ,  H04L12/10 ,  G06F13/42

    CPC classification number: G06F13/387 ,  G06F1/26 ,  G06F1/266 ,  G06F13/42 ,  G06F13/4282 ,  G06F2213/0042 ,  H04L12/10 ,  H04L69/08 ,  Y02D30/30 ,  Y02D30/32

    Abstract: A Network-Universal Serial Bus (NUSB) adaptor exchanges Power-over-Ethernet (PoE) packets with, and receives power from, a Power Source Equipment (PSE) over a PoE connection with the PSE, and exchanges Universal Serial Bus (USB) messages with, and provides power to, a USB device over a USB connection with the USB device. The NUSB adaptor converts between a USB power negotiation protocol implemented between the USB device and the NUSB adaptor and a PoE power negotiation protocol implemented between the NUSB adapter and the network device.

    Abstract translation: 网络通用串行总线（NUSB）适配器通过与PSE的PoE连接与电源设备（PSE）交换以太网供电（PoE）数据包，并从电源设备（PSE）接收电力，并交换通用串行总线（USB） 与USB设备通过USB连接与USB设备通信并提供电源。 NUSB适配器在USB设备和NUSB适配器之间实现的USB功率协商协议和在NUSB适配器和网络设备之间实现的PoE功率协商协议之间进行转换。

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