Partitioned Inference And Training Of Large Models

    公开(公告)号:US20250094798A1

    公开(公告)日:2025-03-20

    申请号:US18727800

    申请日:2022-02-03

    Applicant: Google LLC

    Abstract: Systems and methods for partitioning a large model that has been configured to use a model-synthesis approach in which multiple basis models are combined to generate a final output. The present technology provides systems and methods for identifying a device-specific or subject-specific subset of those basis models to be used on a given device, such that it need not store the weight matrices for the entire set of basis models, and may perform inference using only the weight matrices of the identified subset of basis models. In some examples, the subset of basis models used by a given device may be updated based on actual usage and feedback. Likewise, in some examples, the model may be trained in a federated setting in which multiple devices each utilize different subsets of the basis models, and share training signals with a full copy of the model.

    PERSONALIZED ENTITY REPOSITORY
    2.
    发明申请

    公开(公告)号:US20250024237A1

    公开(公告)日:2025-01-16

    申请号:US18900067

    申请日:2024-09-27

    Applicant: GOOGLE LLC

    Abstract: Systems and methods are provided for a personalized entity repository. For example, a computing device comprises a personalized entity repository having fixed sets of entities from an entity repository stored at a server, a processor, and memory storing instructions that cause the computing device to identify fixed sets of entities that are relevant to a user based on context associated with the computing device, rank the fixed sets by relevancy, and update the personalized entity repository using selected sets determined based on the rank and on set usage parameters applicable to the user. In another example, a method includes generating fixed sets of entities from an entity repository, including location-based sets and topic-based sets, and providing a subset of the fixed sets to a client, the client requesting the subset based on the client's location and on items identified in content generated for display on the client.

    MAPPING IMAGES TO SEARCH QUERIES
    9.
    发明申请

    公开(公告)号:US20220188321A1

    公开(公告)日:2022-06-16

    申请号:US17676615

    申请日:2022-02-21

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus for receiving a query image, receiving one or more entities that are associated with the query image, identifying, for one or more of the entities, one or more candidate search queries that are pre-associated with the one or more entities, generating a respective relevance score for each of the candidate search queries, selecting, as a representative search query for the query image, a particular candidate search query based at least on the generated respective relevance scores and providing the representative search query for output in response to receiving the query image.

    Personalized entity repository
    10.
    发明授权

    公开(公告)号:US11089457B2

    公开(公告)日:2021-08-10

    申请号:US16241704

    申请日:2019-01-07

    Applicant: Google LLC

    Abstract: Systems and methods are provided for a personalized entity repository. For example, a computing device comprises a personalized entity repository having fixed sets of entities from an entity repository stored at a server, a processor, and memory storing instructions that cause the computing device to identify fixed sets of entities that are relevant to a user based on context associated with the computing device, rank the fixed sets by relevancy, and update the personalized entity repository using selected sets determined based on the rank and on set usage parameters applicable to the user. In another example, a method includes generating fixed sets of entities from an entity repository, including location-based sets and topic-based sets, and providing a subset of the fixed sets to a client, the client requesting the subset based on the client's location and on items identified in content generated for display on the client.

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