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    Sparse recovery autoencoder
    12.
    发明授权
    Sparse recovery autoencoder 有权

    公开(公告)号：US12033080B2

    公开(公告)日：2024-07-09

    申请号：US16442203

    申请日：2019-06-14

    Applicant: GOOGLE LLC

    Inventor： Xinnan Yu ,  Shanshan Wu ,  Daniel Holtmann-Rice ,  Dmitry Storcheus ,  Sanjiv Kumar ,  Afshin Rostamizadeh

    IPC: G06N3/084 ,  G06F17/16 ,  G06N3/02 ,  G06N3/045

    CPC classification number: G06N3/084 ,  G06F17/16 ,  G06N3/02 ,  G06N3/045

    Abstract: A sparse dataset is encoded using a data-driven learned sensing matrix. For example, an example method includes receiving a dataset of sparse vectors with dimension d from a requesting process, initializing an encoding matrix of dimension k×d, selecting a subset of sparse vectors from the dataset, and updating the encoding matrix via machine learning. Updating the encoding matrix includes using a linear encoder to generate an encoded vector of dimension k for each vector in the subset, the linear encoder using the encoding matrix, using a non-linear decoder to decode each of the encoded vectors, the non-linear decoder using a transpose of the encoding matrix in a projected subgradient, and adjusting the encoding matrix using back propagation. The method also includes returning an embedding of each sparse vector in the dataset of sparse vectors, the embedding being generated with the updated encoding matrix.

    Communication efficient federated learning
    13.
    发明授权
    Communication efficient federated learning 有权

    公开(公告)号：US11763197B2

    公开(公告)日：2023-09-19

    申请号：US16850053

    申请日：2020-04-16

    Applicant: Google LLC

    Inventor： Hugh Brendan McMahan ,  Dave Morris Bacon ,  Jakub Konecny ,  Xinnan Yu

    IPC: G06N20/00 ,  G06F17/16 ,  G06F17/18 ,  H04L67/01 ,  G06N7/01 ,  G06F7/58

    CPC classification number: G06N20/00 ,  G06F17/16 ,  G06F17/18 ,  G06N7/01 ,  H04L67/01 ,  G06F7/582

    Abstract: The present disclosure provides efficient communication techniques for transmission of model updates within a machine learning framework, such as, for example, a federated learning framework in which a high-quality centralized model is trained on training data distributed overt a large number of clients each with unreliable network connections and low computational power. In an example federated learning setting, in each of a plurality of rounds, each client independently updates the model based on its local data and communicates the updated model back to the server, where all the client-side updates are used to update a global model. The present disclosure provides systems and methods that reduce communication costs. In particular, the present disclosure provides at least: structured update approaches in which the model update is restricted to be small and sketched update approaches in which the model update is compressed before sending to the server.

    Multiscale Quantization for Fast Similarity Search
    14.
    发明申请
    Multiscale Quantization for Fast Similarity Search 有权

    公开(公告)号：US20230123941A1

    公开(公告)日：2023-04-20

    申请号：US18081376

    申请日：2022-12-14

    Applicant: Google LLC

    Inventor： Xiang Wu ,  David Simcha ,  Daniel Holtmann-Rice ,  Sanjiv Kumar ,  Ananda Theertha Suresh ,  Ruiqi Guo ,  Xinnan Yu

    IPC: G06F16/33 ,  G06F16/31 ,  G06N20/00

    Abstract: The present disclosure provides systems and methods that include or otherwise leverage use of a multiscale quantization model that is configured to provide a quantized dataset. In particular, the multiscale quantization model can receive and perform vector quantization of a first dataset. The multiscale quantization model can generate a residual dataset based at least in part on a result of the vector quantization. The multiscale quantization model can apply a rotation matrix to the residual dataset to generate a rotated residual dataset that includes a plurality of rotated residuals. The multiscale quantization model can perform reparameterization of each rotated residual in the rotated residual dataset into a direction component and a scale component. The multiscale quantization model can perform product quantization of the direction components of the plurality of rotated residuals, and perform scalar quantization of the scale components of the plurality of rotated residuals.

    Fast orthogonal projection
    15.
    发明授权
    Fast orthogonal projection 有权

    公开(公告)号：US10394777B2

    公开(公告)日：2019-08-27

    申请号：US14951909

    申请日：2015-11-25

    Applicant: Google LLC

    Inventor： Xinnan Yu ,  Sanjiv Kumar ,  Ruiqi Guo

    IPC: G06F17/00 ,  G06F16/22 ,  G06F16/33 ,  G06F16/951

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for efficiently performing linear projections. In one aspect, a method includes actions for obtaining a plurality of content items from one or more content sources. Additional actions include, extracting a plurality of features from each of the plurality of content items, generating a feature vector for each of the extracted features in order to create a search space, generating a series of element matrices based upon the generated feature vectors, transforming the series of element matrices into a structured matrix such that the transformation preserves one or more relationships associated with each element matrix of the series of element matrices, receiving a search object, searching the enhanced search space based on the received search object, provided one or more links to a content item that are responsive to the search object.

    Federated Learning with Only Positive Labels
    18.
    发明申请
    Federated Learning with Only Positive Labels 有权

    公开(公告)号：US20210326757A1

    公开(公告)日：2021-10-21

    申请号：US17227851

    申请日：2021-04-12

    Applicant: Google LLC

    Inventor： Ankit Singh Rawat ,  Xinnan Yu ,  Aditya Krishna Menon ,  Sanjiv Kumar

    IPC: G06N20/00 ,  G06F17/16 ,  G06N5/02

    Abstract: Generally, the present disclosure is directed to systems and methods that perform spreadout regularization to enable learning of a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a limited number of classes (e.g., a single class). Examples of such settings include decentralized training of face recognition models or speaker identification models, where in addition to the user specific facial images and voice samples, the class embeddings for the users also constitute sensitive information that cannot be shared with other users.

    SPARSE RECOVERY AUTOENCODER
    20.
    发明申请
    SPARSE RECOVERY AUTOENCODER 审中-公开

    公开(公告)号：US20190385063A1

    公开(公告)日：2019-12-19

    申请号：US16442203

    申请日：2019-06-14

    Applicant: GOOGLE LLC

    Inventor： Xinnan Yu ,  Shanshan Wu ,  Daniel Holtmann-Rice ,  Dmitry Storcheus ,  Sanjiv Kumar ,  Afshin Rostamizadeh

    IPC: G06N3/08 ,  G06N3/04 ,  G06F17/16

    Abstract: A sparse dataset is encoded using a data-driven learned sensing matrix. For example, an example method includes receiving a dataset of sparse vectors with dimension d from a requesting process, initializing an encoding matrix of dimension k×d, selecting a subset of sparse vectors from the dataset, and updating the encoding matrix via machine learning. Updating the encoding matrix includes using a linear encoder to generate an encoded vector of dimension k for each vector in the subset, the linear encoder using the encoding matrix, using a non-linear decoder to decode each of the encoded vectors, the non-linear decoder using a transpose of the encoding matrix in a projected subgradient, and adjusting the encoding matrix using back propagation. The method also includes returning an embedding of each sparse vector in the dataset of sparse vectors, the embedding being generated with the updated encoding matrix.

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