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    CLASSIFYING TIME SERIES USING RECONSTRUCTION ERRORS
    1.
    发明公开
    CLASSIFYING TIME SERIES USING RECONSTRUCTION ERRORS 审中-公开

    公开(公告)号：US20230380771A1

    公开(公告)日：2023-11-30

    申请号：US17825427

    申请日：2022-05-26

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  David Passey

    IPC: A61B5/00

    CPC classification number: A61B5/7267 ,  A61B5/7275 ,  A61B5/7285 ,  A61B5/725

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for classifying an input time series into a class from a set of classes. In one aspect, a method comprises: receiving an input time series; processing the input time series using a reconstruction model to generate a reconstruction model output that comprises a plurality of channels, wherein each channel of the plurality of channels defines a respective output time series, and wherein each channel of the plurality of channels corresponds to a respective class from the set of classes; determining a respective reconstruction error for each channel of the plurality of channels based on an error between: (i) the output time series defined by the channel, and (ii) the input time series; and classifying the input time series as being included in a class from the set of classes based on the reconstruction errors.

    Artificial neural network architectures based on synaptic connectivity graphs
    2.
    发明授权
    Artificial neural network architectures based on synaptic connectivity graphs 有权

    公开(公告)号：US11593627B2

    公开(公告)日：2023-02-28

    申请号：US16731396

    申请日：2019-12-31

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  Philip Edwin Watson

    IPC: G06N3/063 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an artificial neural network architecture based on a synaptic connectivity graph. According to one aspect, there is provided a method comprising: obtaining a synaptic resolution image of at least a portion of a brain of a biological organism; processing the image to identify: (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain; generating data defining a graph representing synaptic connectivity between the neurons in the brain; determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain; and processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

    Reservoir computing neural networks based on synaptic connectivity graphs
    3.
    发明授权
    Reservoir computing neural networks based on synaptic connectivity graphs 有权

    公开(公告)号：US11593617B2

    公开(公告)日：2023-02-28

    申请号：US16776574

    申请日：2020-01-30

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  Philip Edwin Watson ,  Georgios Evangelopoulos

    IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00 ,  G06V10/82 ,  G06V30/18

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

    DATA ANALYTIC APPROACH TO PERSONALIZED QUESTIONNAIRE DEVELOPMENTS
    4.
    发明申请
    DATA ANALYTIC APPROACH TO PERSONALIZED QUESTIONNAIRE DEVELOPMENTS 有权

    公开(公告)号：US20210391039A1

    公开(公告)日：2021-12-16

    申请号：US16902422

    申请日：2020-06-16

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  Nina Thigpen ,  Katherine Elise Link ,  Vladimir Miskovic

    IPC: G16H10/20 ,  G06F16/28 ,  G16H50/70 ,  G16H50/20 ,  G16H10/60 ,  G06F40/30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a plurality of answers to a first set of questions. The actions include generating an adjacency matrix based on the question-answer pairs. The actions include determining a network graph that includes question nodes and edges. The actions include identifying one or more clusters of question nodes by applying a community detection algorithm on the network graph. The actions include determining, for each cluster, i) a cluster centrality and ii) a cluster magnitude. The actions include ranking the clusters based on the cluster centralities and the cluster magnitudes of the one or more clusters. The actions include selecting a second set of questions for the user. And, the actions include causing the questions from the second set of questions to be presented to the user.

    NEURAL ARCHITECTURE SEARCH BASED ON SYNAPTIC CONNECTIVITY GRAPHS
    5.
    发明申请
    NEURAL ARCHITECTURE SEARCH BASED ON SYNAPTIC CONNECTIVITY GRAPHS 有权

    公开(公告)号：US20210201107A1

    公开(公告)日：2021-07-01

    申请号：US16776108

    申请日：2020-01-29

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  Philip Edwin Watson ,  Georgios Evangelopoulos

    IPC: G06N3/00 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06K9/46 ,  G10L25/51 ,  G10L25/30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a neural network architecture for performing a machine learning task. In one aspect, a method comprises: obtaining data defining a synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism; generating data defining a plurality of candidate graphs based on the synaptic connectivity graph; determining, for each candidate graph, a performance measure on a machine learning task of a neural network having a neural network architecture that is specified by the candidate graph; and selecting a final neural network architecture for performing the machine learning task based on the performance measures.

    ARTIFICIAL NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY GRAPHS
    9.
    发明公开
    ARTIFICIAL NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY GRAPHS 审中-公开

    公开(公告)号：US20230229901A1

    公开(公告)日：2023-07-20

    申请号：US18173157

    申请日：2023-02-23

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  Philip Edwin Watson

    IPC: G06N3/063 ,  G06N3/04

    CPC classification number: G06N3/063 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an artificial neural network architecture based on a synaptic connectivity graph. According to one aspect, there is provided a method comprising: obtaining a synaptic resolution image of at least a portion of a brain of a biological organism; processing the image to identify: (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain; generating data defining a graph representing synaptic connectivity between the neurons in the brain; determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain; and processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

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