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    Dynamic training of Models
    4.
    发明公开
    Dynamic training of Models 审中-公开

    公开(公告)号：US20240029413A1

    公开(公告)日：2024-01-25

    申请号：US18350845

    申请日：2023-07-12

    Applicant: Google LLC

    Inventor： Anthony Jacob Piergiovanni ,  Weiching Kuo ,  Wei Li ,  Anelia Angelova

    IPC: G06V10/774 ,  G06V10/25

    CPC classification number: G06V10/774 ,  G06V10/25 ,  G06V2201/07

    Abstract: A method involves the training of a model by dynamically adjusting the number of examples within each training batch. The dynamic adjustment is accomplished by adjusting the number of examples per task within each training batch according to the performance of the model on the tasks that the model is being trained on. In some embodiments, this method is applied to cross-modal vision-language tasks. This model may also be applied to the pre-training of a model that can be later fine-tuned for a more specific task(s).

    NEURAL NETWORK MODELS USING PEER-ATTENTION
    5.
    发明申请
    NEURAL NETWORK MODELS USING PEER-ATTENTION 有权

    公开(公告)号：US20230114556A1

    公开(公告)日：2023-04-13

    申请号：US17909581

    申请日：2021-07-14

    Applicant: Google LLC

    Inventor： Michael Sahngwon Ryoo ,  Anthony Jacob Piergiovanni ,  Anelia Angelova

    IPC: G06V10/82 ,  G06V10/26

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing a network input using a neural network to generate a network output. In one aspect, a method comprises processing a network input sing a neural network to generate a network output, where the neural network has multiple blocks, wherein each block is configured to process a block input to generate a block output, the method comprising, for each target block of the neural network: generating attention-weighted representations of multiple first block outputs, comprising, for each first block output: processing multiple second block outputs to generate attention factors; and generating the attention-weighted representation of each first block output by applying the respective attention factors to the corresponding first block output; and generating the target block input from the attention-weighted representations; and processing the target block input using the target block to generate a target block output.

    CONNECTION WEIGHT LEARNING FOR GUIDED ARCHITECTURE EVOLUTION
    7.
    发明申请
    CONNECTION WEIGHT LEARNING FOR GUIDED ARCHITECTURE EVOLUTION 有权

    公开(公告)号：US20220189154A1

    公开(公告)日：2022-06-16

    申请号：US17605783

    申请日：2020-05-22

    Applicant: Google LLC

    Inventor： Michael Sahngwon Ryoo ,  Anthony Jacob Piergiovanni ,  Mingxing Tan ,  Anelia Angelova

    IPC: G06V10/82 ,  G06N3/04 ,  G06T7/207 ,  G06T1/20 ,  G06T3/40 ,  G06V10/776

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining one or more neural network architectures of a neural network for performing a video processing neural network task. In one aspect, a method comprises: at each of a plurality of iterations: selecting a parent neural network architecture from a set of neural network architectures; training a neural network having the parent neural network architecture to perform the video processing neural network task, comprising determining trained values of connection weight parameters of the parent neural network architecture; generating a new neural network architecture based at least in part on the trained values of the connection weight parameters of the parent neural network architecture; and adding the new neural network architecture to the set of neural network architectures.

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