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    SUBJECT-OBJECT INTERACTION RECOGNITION MODEL
    1.
    发明申请
    SUBJECT-OBJECT INTERACTION RECOGNITION MODEL 审中-公开

    公开(公告)号：US20200302232A1

    公开(公告)日：2020-09-24

    申请号：US16827592

    申请日：2020-03-23

    Applicant: QUALCOMM Technologies, Inc.

    Inventor： Mert KILICKAYA ,  Efstratios GAVVES ,  Arnold Wilhelmus Maria SMEULDERS

    IPC: G06K9/62 ,  G06N3/08

    Abstract: A method for processing an image is presented. The method locates a subject and an object of a subject-object interaction in the image. The method determines relative weights of the subject, the object, and a context region for classification. The method further classifies the subject-object interaction based on a classification of a weighted representation of the subject, a weighted representation of the object, and a weighted representation of the context region.

    COMBINATORIAL BAYESIAN OPTIMIZATION USING A GRAPH CARTESIAN PRODUCT
    3.
    发明申请
    COMBINATORIAL BAYESIAN OPTIMIZATION USING A GRAPH CARTESIAN PRODUCT 有权

    公开(公告)号：US20210034928A1

    公开(公告)日：2021-02-04

    申请号：US16945625

    申请日：2020-07-31

    Applicant: QUALCOMM Technologies, Inc.

    Inventor： Changyong OH ,  Efstratios GAVVES ,  Jakub Mikolaj TOMCZAK ,  Max WELLING

    IPC: G06K9/62 ,  G06N7/00

    Abstract: Certain aspects provide a method for determining a solution to a combinatorial optimization problem, including: determining a plurality of subgraphs, wherein each subgraph of the plurality of subgraphs corresponds to a combinatorial variable of the plurality of combinatorial variables; determining a combinatorial graph based on the plurality of subgraphs; determining evaluation data comprising a set of vertices in the combinatorial graph and evaluations on the set of vertices; fitting a Gaussian process to the evaluation data; determining an acquisition function for vertices in the combinatorial graph using a predictive mean and a predictive variance from the fitted Gaussian process; optimizing the acquisition function on the combinatorial graph to determine a next vertex to evaluate; evaluating the next vertex; updating the evaluation data with a tuple of the next vertex and its evaluation; and determining a solution to the problem, wherein the solution comprises a vertex of the combinatorial graph.

    EFFICIENT NEURAL CAUSAL DISCOVERY
    4.
    发明公开
    EFFICIENT NEURAL CAUSAL DISCOVERY 审中-公开

    公开(公告)号：US20240176994A1

    公开(公告)日：2024-05-30

    申请号：US18551844

    申请日：2021-07-26

    Applicant: QUALCOMM TECHNOLOGIES, INC.

    Inventor： Phillip LIPPE ,  Taco Sebastiaan COHEN ,  Efstratios GAVVES

    IPC: G06N3/0464 ,  G06N3/09

    CPC classification number: G06N3/0464 ,  G06N3/09

    Abstract: A method for generating a causal graph includes receiving a data set including observation data and intervention data corresponding to multiple variables. A probability distribution is determined for each variable based on the observation data. A likelihood of including each edge in the graph is computed based on the probability distribution and the intervention data. Each edge is a causal connection between variables of the multiple variables. The graph is generated based on the likelihood of including each edge. The graph may be updated by iteratively repeating the determination of the probability distribution and the computing of the likelihood of including each edge.

    Batch Softmax For 0-Label And Multilabel Classification
    9.
    发明公开
    Batch Softmax For 0-Label And Multilabel Classification 审中-公开

    公开(公告)号：US20240303477A1

    公开(公告)日：2024-09-12

    申请号：US17754906

    申请日：2020-11-16

    Applicant: QUALCOMM Technologies, Inc.

    Inventor： Shuai LIAO ,  Efstratios GAVVES ,  Cornelis Gerardus Maria SNOEK

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Embodiments include methods, and processing devices for implementing the methods. Various embodiments may include calculating a batch softmax normalization factor using a plurality of logit values from a plurality of logits of a layer of a neural network, normalizing the plurality of logit values using the batch softmax normalization factor, and mapping each of the normalized plurality of logit values to one of a plurality of manifolds in a coordinate space. In some embodiments, each of the plurality of manifolds represents a number of labels to which a logit can be classified. In some embodiments, at least one of the plurality of manifolds represents a number of labels other than one label.

    FEDERATED MIXTURE MODELS
    10.
    发明申请
    FEDERATED MIXTURE MODELS 有权

    公开(公告)号：US20230118025A1

    公开(公告)日：2023-04-20

    申请号：US17914297

    申请日：2021-06-03

    Applicant: QUALCOMM Technologies, Inc.

    Inventor： Matthias REISSER ,  Max WELLING ,  Efstratios GAVVES ,  Christos LOUIZOS

    IPC: G06N3/098 ,  G06N3/045

    Abstract: A method of collaboratively training a neural network model, includes receiving a local update from a subset of the multiple users. The local update is related to one or more subsets of a dataset of the neural network model. A local component of the neural network model identifies a subset of the one or more subsets to which a data point belongs. A global update is computed for the neural network model based on the local updates from the subset of the users. The global updates for each portion of the network are aggregated to train the neural network model.

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