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    METHOD AND APPARATUS FOR PHOTOGRAPHING USING ELECTRONIC DEVICE CAPABLE OF FLYING
    1.
    发明申请
    METHOD AND APPARATUS FOR PHOTOGRAPHING USING ELECTRONIC DEVICE CAPABLE OF FLYING 审中-公开

    公开(公告)号：US20170134699A1

    公开(公告)日：2017-05-11

    申请号：US15271536

    申请日：2016-09-21

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Minyoung KIM ,  Seung-Nyun KIM ,  So-Young LEE

    IPC: H04N7/18 ,  G08B5/38

    CPC classification number: H04N7/183 ,  B64C39/024 ,  B64C2201/123 ,  B64C2201/127 ,  B64C2201/141 ,  B64C2201/146

    Abstract: An electronic device and flying device and respective methods are disclosed. The electronic device includes a communication circuit, a position notification device, one or more sensors, a processor which executes the method, including transmitting a first signal to the flying device to recognize an image signal generated by the electronic device, generating the image signal, receiving a second signal indicating that the flying device recognized the image signal, and transmitting a third signal to control movement of the flying device. The flying device includes a camera, a communication circuit, one or more sensors, a processor which executes a method, including receiving a first signal from the electronic device, controlling the camera to detect an image signal generated by the electronic device for determining a position of the electronic device, and controlling movement of the flying device based on a second signal and the determined position of the electronic device.

    METHOD AND APPARATUS FOR GENERATING A NOISE-RESILIENT MACHINE LEARNING MODEL
    3.
    发明公开
    METHOD AND APPARATUS FOR GENERATING A NOISE-RESILIENT MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20240330685A1

    公开(公告)日：2024-10-03

    申请号：US18742494

    申请日：2024-06-13

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Minyoung KIM ,  Timothy HOSPEDALES ,  Da LI ,  Xu HU

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: The present application relates to a computer-implemented method for an improved technique for optimising the loss function during deep learning. The method includes receiving a training data set comprising a plurality of data items, initialising weights of at least one neural network layer of the ML model, and training, using an iterative process, the at least one neural network layer of the ML model by inputting, into the at least one neural network layer, the plurality of data items, processing the plurality of data items using the at least one neural network layer and the weights, optimising a loss function of the weights by simultaneously minimising a loss value and a loss sharpness using weights that lie in a neighbourhood having a similar low loss value, wherein the neighbourhood is determined by a geometry of a parameter space defined by the weights of the ML model, and updating the weights of the at least one neural network layer using the optimised loss function.

    SEMICONDUCTOR DEVICES HAVING CAPACITOR STRUCTURES AND SEMICONDUCTOR INTEGRATED CIRCUITS HAVING THE SAME
    4.
    发明申请
    SEMICONDUCTOR DEVICES HAVING CAPACITOR STRUCTURES AND SEMICONDUCTOR INTEGRATED CIRCUITS HAVING THE SAME 有权

    公开(公告)号：US20240421068A1

    公开(公告)日：2024-12-19

    申请号：US18738100

    申请日：2024-06-10

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Myoungsoo KIM ,  Minyoung KIM

    IPC: H01L23/522 ,  H01L27/02 ,  H01L27/06

    Abstract: A semiconductor device including: a device layer on a substrate; an interconnection layer disposed on the device layer, wherein the interconnection layer includes conductive interconnections forming a plurality of layers; and first and second capacitor structures disposed inside the interconnection layer. Each of the first and second capacitor structures includes: electrode layers spaced apart from each other in a vertical direction and forming three or more layers; dielectric layers between the electrode layers; conductive vias respectively connected to one of the electrode layers and extending vertically; a first connection terminal electrically connected to a lowermost electrode layer; and a second connection terminal electrically connected to at least one of the electrode layers, wherein the first capacitor structure and the second capacitor structure include the same number of electrode layers, and wherein a first capacitance of the first capacitor structure is different from a second capacitance of the second capacitor structure.

    END CAP HOLDER FOR A GAS CYLINDER
    7.
    发明申请
    END CAP HOLDER FOR A GAS CYLINDER 有权

    公开(公告)号：US20230009496A1

    公开(公告)日：2023-01-12

    申请号：US17677452

    申请日：2022-02-22

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Minyoung KIM ,  Kwangjun KIM ,  Kyeongsup BYEON ,  Byungjun AN ,  Huigwan LEE ,  Byeongchan JEONG ,  Hyungho CHOI

    IPC: F17C13/06 ,  F17C13/04

    Abstract: An end cap holder may include a base block and a holding block. The base block may be configured to provide an end cap, which may be at a nozzle of the gas cylinder, with a torque for combining/separating the end cap with/from the nozzle. An angle correction groove may be formed at a first surface of the base block oriented toward the end cap. The holding block may be rotatably receivable in the angle correction groove to hold the end cap. The holding block may selectively make point contact with the base block to transmit the torque of the base block to the end cap. Thus, the holding block may accurately hold the end cap tilted to a vertical axis or a horizontal axis.

    METHOD AND SYSTEM FOR FEDERATED LEARNING
    8.
    发明公开
    METHOD AND SYSTEM FOR FEDERATED LEARNING 审中-公开

    公开(公告)号：US20240135194A1

    公开(公告)日：2024-04-25

    申请号：US18512195

    申请日：2023-11-17

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Minyoung KIM ,  Timothy HOSPEDALES

    IPC: G06N3/098

    CPC classification number: G06N3/098

    Abstract: Broadly speaking, embodiments of the present techniques provide a method for training a machine learning, ML, model to update global and local versions of a model. We propose a novel hierarchical Bayesian approach to Federated Learning (FL), where our models reasonably describe the generative process of clients' local data via hierarchical Bayesian modeling: constituting random variables of local models for clients that are governed by a higher-level global variate. Interestingly, the variational inference in our Bayesian model leads to an optimisation problem whose block-coordinate descent solution becomes a distributed algorithm that is separable over clients and allows them not to reveal their own private data at all, thus fully compatible with FL.

    METHOD AND APPARATUS FOR CONCEPT MATCHING
    9.
    发明申请
    METHOD AND APPARATUS FOR CONCEPT MATCHING 有权

    公开(公告)号：US20230137671A1

    公开(公告)日：2023-05-04

    申请号：US17434314

    申请日：2021-08-20

    Applicant: SAMSUNG ELECTRONICS CO., LTD.

    Inventor： Vladimir PAVLOVIC ,  Minyoung KIM ,  Ricardo GUERRERO ,  Hai Xuan PHAM

    IPC: G06F16/9535 ,  G06F16/9538 ,  G06F3/0482 ,  G06F18/22 ,  G06N3/08

    Abstract: A computer-implemented method for concept matching using a machine learning model, may include: receiving, from a user, a search query comprising: at least one criterion that represents at least one concept; inputting the received at least our criterion into at least one neural network for processing the search query; determining, using the at least one neural network, the at least one concept represented by the at least one criterion; retrieving, from a storage, at least one data item winch matches the determined at least one concept, through a cross-modal data retrieval method of retrieving a data type different from an input data type; and outputting the retrieved at least one data item in response to the search query.

    METHOD AND APPARATUS FOR META FEW-SHOT LEARNER
    10.
    发明申请
    METHOD AND APPARATUS FOR META FEW-SHOT LEARNER 有权

    公开(公告)号：US20230117307A1

    公开(公告)日：2023-04-20

    申请号：US17843590

    申请日：2022-06-17

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Minyoung KIM ,  Timothy HOSPEDALES

    IPC: G06V10/776 ,  G06V10/82 ,  G06V10/70 ,  G06V10/774

    Abstract: The subject-matter of the present disclosure relates to a computer-implemented method of training a machine learning, ML, meta learner classifier model to perform few-shot image or speech classification, the method comprising: training the machine learning, ML, meta learner classifier model by: iteratively obtaining a support set and a query set of a current episode; adapting the model using the support set; measuring a performance of the adapted model using the query set; and updating the classifier based on the performance; wherein adapting the model using the support set comprises: deriving a Laplace approximated posterior using a linear classifier based on Gaussian mixture fitting; and deriving a predictive distribution using the approximated posterior; wherein measuring the performance of the adapted model using the query set comprises: determining a loss associated with the predictive distribution using the query set; and wherein updating the classifier based on the performance comprises minimising the loss.

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