中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

13 records (took 0.014 seconds)

    Anti-fragile network
    1.
    发明授权
    Anti-fragile network 有权

    公开(公告)号：US11706111B1

    公开(公告)日：2023-07-18

    申请号：US17732957

    申请日：2022-04-29

    Applicant: X Development LLC

    Inventor： John Michael Stivoric ,  David Andre ,  Ryan Butterfoss ,  Rebecca Radkoff ,  Salil Vijaykumar Pradhan ,  Grace Taixi Brentano ,  Lam Thanh Nguyen

    IPC: H04L43/065 ,  H04L41/0604 ,  H04L41/12 ,  H04L43/0817

    CPC classification number: H04L43/065 ,  H04L41/0627 ,  H04L41/12 ,  H04L43/0817

    Abstract: Implementations are directed to improving network anti-fragility. In some aspects, a method includes receiving parameter data from a network of nodes, the parameter data comprising attributes, policies, and action spaces for each node in the network of nodes; configuring one or more interruptive events on one or more nodes included in the network of nodes; determining a first action of each node in the network of nodes in response to the one or more interruptive events; determining a first performance metric, for each node, that corresponds to the first action, wherein the first performance matric is determined based on at least a first reward value associated with the first action; continuously updating the first action in an iterative process to obtain a final action, wherein a performance metric corresponding to the final action satisfies a performance threshold, and transmitting the final action for each node to the network of nodes.

    COMPRESSED MATRIX REPRESENTATIONS OF NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY
    3.
    发明申请
    COMPRESSED MATRIX REPRESENTATIONS OF NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY 有权

    公开(公告)号：US20230004791A1

    公开(公告)日：2023-01-05

    申请号：US17364444

    申请日：2021-06-30

    Applicant: X Development LLC

    Inventor： Yu Feng ,  Lam Thanh Nguyen

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing brain emulation neural networks using compressed matrix representations. One of the methods includes obtaining a network input; and processing the network input using a neural network to generate a network output, comprising: processing the network input using an input subnetwork of the neural network to generate an embedding of the network input; and processing the embedding of the network input using a brain emulation subnetwork of the neural network, wherein the brain emulation subnetwork has a brain emulation neural network architecture that represents synaptic connectivity between a plurality of biological neurons in a brain of a biological organism, the processing comprising: obtaining a compressed matrix representation of a sparse matrix of brain emulation parameters; and applying the compressed matrix representation to the embedding of the network input to generate a brain emulation subnetwork output.

    AUTOMATICALLY DETERMINING NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY
    4.
    发明申请
    AUTOMATICALLY DETERMINING NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY 有权

    公开(公告)号：US20220414433A1

    公开(公告)日：2022-12-29

    申请号：US17362721

    申请日：2021-06-29

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  Lam Thanh Nguyen

    IPC: G06N3/06 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining network architectures based on synaptic connectivity. One of the methods includes processing a network input using a neural network to generate a network output, comprising: processing the network input using an encoder subnetwork of the neural network to generate an embedding of the network input; processing the embedding of the network input using a first connectivity layer of the neural network to generate a first connectivity layer output; processing the first connectivity layer output using a brain emulation subnetwork of the neural network to generate a brain emulation subnetwork output; processing the brain emulation subnetwork output using a second connectivity layer of the neural network to generate a second connectivity layer output; and processing the second connectivity layer output using a decoder subnetwork of the neural network to generate the network output.

    DEFECT DETECTION USING NEURAL NETWORKS BASED ON BIOLOGICAL CONNECTIVITY
    5.
    发明公开
    DEFECT DETECTION USING NEURAL NETWORKS BASED ON BIOLOGICAL CONNECTIVITY 审中-公开

    公开(公告)号：US20230196541A1

    公开(公告)日：2023-06-22

    申请号：US17559641

    申请日：2021-12-22

    Applicant: X Development LLC

    Inventor： Sarah Ann Laszlo ,  Lam Thanh Nguyen ,  Baihan Lin

    IPC: G06T7/00 ,  G06N3/04 ,  G06N3/00

    CPC classification number: G06T7/0004 ,  G06N3/0454 ,  G06N3/008 ,  G06T2207/20084 ,  G06T2207/30108 ,  G06T2207/20081

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing defect detection using brain emulation neural networks. One of the methods includes obtaining an image of a manufactured article; processing the image of the manufactured article using an encoder subnetwork of a defect detection neural network to generate an encoder subnetwork output; processing the encoder subnetwork output using a brain emulation subnetwork of the defect detection neural network to generate a brain emulation subnetwork output, wherein the brain emulation subnetwork has an architecture that comprises brain emulation parameters that, when initialized, represent biological connectivity between biological neuronal elements in a brain of a biological organism; processing the brain emulation subnetwork output using a decoder subnetwork of the defect detection neural network to generate a network output that predicts whether the manufactured article includes a defect; and taking an action based on the network output.

    IMPLEMENTING NEURAL NETWORKS THAT INCLUDE CONNECTIVITY NEURAL NETWORK LAYERS USING SYNAPTIC CONNECTIVITY
    7.
    发明申请
    IMPLEMENTING NEURAL NETWORKS THAT INCLUDE CONNECTIVITY NEURAL NETWORK LAYERS USING SYNAPTIC CONNECTIVITY 有权

    公开(公告)号：US20220414434A1

    公开(公告)日：2022-12-29

    申请号：US17362747

    申请日：2021-06-29

    Applicant: X Development LLC

    Inventor： Lam Thanh Nguyen

    IPC: G06N3/06 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing connectivity neural network layers. One of the methods includes processing a network input using a neural network to generate a network output, comprising: generating a layer input to a connectivity layer of the neural network based on the network input, wherein the layer input to the connectivity layer comprises a plurality of input values arranged in a plurality of input channels; processing the layer input using the connectivity layer to generate a layer output comprising a plurality of output values arranged in a plurality of output channels; processing the plurality of output channels of the connectivity layer using a brain emulation subnetwork of the neural network to generate a brain emulation subnetwork output; and generating the network output based on the brain emulation subnetwork output.

    GENERATING ACTIONS FOR A SUPPLY CHAIN NETWORK
    8.
    发明申请
    GENERATING ACTIONS FOR A SUPPLY CHAIN NETWORK 有权

    公开(公告)号：US20250131366A1

    公开(公告)日：2025-04-24

    申请号：US18926132

    申请日：2024-10-24

    Applicant: X Development LLC

    Inventor： Lam Thanh Nguyen ,  Grace Taixi Brentano ,  Sze Man Lee ,  Karush Suri ,  Anikait Singh ,  Salil Vijaykumar Pradhan ,  David Andre ,  Gearoid Murphy

    IPC: G06Q10/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating actions for a supply chain network. One of the methods includes receiving a request to generate an action in a supply chain network for a particular product based on current state information; providing a request to an action model to generate a respective probability distribution for one or more actions for one or more products; receiving, from the action model, the respective probability distributions for the one or more products; determining, for each product, a binned action from the respective probability distribution; providing a request to a sequence model to generate a respective correction for the one or more binned actions; and receiving, from the sequence model, the respective correction for the respective binned action.

    SYNTHESIS AND AUGMENTATION OF TRAINING DATA FOR SUPPLY CHAIN OPTIMIZATION
    9.
    发明公开
    SYNTHESIS AND AUGMENTATION OF TRAINING DATA FOR SUPPLY CHAIN OPTIMIZATION 审中-公开

    公开(公告)号：US20240330743A1

    公开(公告)日：2024-10-03

    申请号：US18129416

    申请日：2023-03-31

    Applicant: X Development LLC

    Inventor： David Andre ,  Grace Taixi Brentano ,  Lam Thanh Nguyen ,  Salil Vijaykumar Pradhan ,  Peter Michael Aronow

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating synthetic training data representing network disruptions. One of the methods includes obtaining data representing one or more first travel time distributions between at the at least two entities in the supply chain network. Synthetic network disruption data is generated including sampling from one or more second travel time distributions corresponding respectively to one or more simulated network disruptions. A second dataset having the synthetic network disruption data is generated, and a network policy agent is trained using the second dataset.

Patent Agency Ranking