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    Learning compressible features
    2.
    发明授权
    Learning compressible features 有权

    公开(公告)号：US11610124B2

    公开(公告)日：2023-03-21

    申请号：US16666689

    申请日：2019-10-29

    申请人： Google LLC

    发明人： Abhinav Shrivastava ,  Saurabh Singh ,  Johannes Balle ,  Sami Ahmad Abu-El-Haija ,  Nicholas Johnston ,  George Dan Toderici

    IPC分类号： G06K9/00 ,  G06N3/082 ,  G06F17/15 ,  G06K9/62 ,  G06N3/063

    摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, by a neural network (NN), a dataset for generating features from the dataset. A first set of features is computed from the dataset using at least a feature layer of the NN. The first set of features i) is characterized by a measure of informativeness; and ii) is computed such that a size of the first set of features is compressible into a second set of features that is smaller in size than the first set of features and that has a same measure of informativeness as the measure of informativeness of the first set of features. The second set of features if generated from the first set of features using a compression method that compresses the first set of features to generate the second set of features.

    LEARNING COMPRESSIBLE FEATURES
    3.
    发明公开
    LEARNING COMPRESSIBLE FEATURES 审中-公开

    公开(公告)号：US20230237332A1

    公开(公告)日：2023-07-27

    申请号：US18175125

    申请日：2023-02-27

    申请人： GOOGLE LLC

    发明人： Abhinav Shrivastava ,  Saurabh Singh ,  Johannes Ballé ,  Sami Ahmad Abu-El-Haija ,  Nicholas Milo Johnston ,  George Dan Toderici

    IPC分类号： G06N3/08 ,  G06F17/15 ,  G06N3/063

    CPC分类号： G06N3/08 ,  G06F17/15 ,  G06F18/24 ,  G06N3/063 ,  G06N3/082

    摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, by a neural network (NN), a dataset for generating features from the dataset. A first set of features is computed from the dataset using at least a feature layer of the NN. The first set of features i) is characterized by a measure of informativeness; and ii) is computed such that a size of the first set of features is compressible into a second set of features that is smaller in size than the first set of features and that has a same measure of informativeness as the measure of informativeness of the first set of features. The second set of features if generated from the first set of features using a compression method that compresses the first set of features to generate the second set of features.

    Representing graph edges using neural networks
    4.
    发明授权
    Representing graph edges using neural networks 有权

    公开(公告)号：US11455512B1

    公开(公告)日：2022-09-27

    申请号：US15946301

    申请日：2018-04-05

    申请人： Google LLC

    发明人： Rami Al-rfou′ ,  Sami Ahmad Abu-El-Haija ,  Bryan Thomas Perozzi

    IPC分类号： G06N3/04 ,  G06N3/08 ,  G06F16/901

    摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a graph processing system. In one aspect, the graph processing system obtains data identifying a first node and a second node from a graph of nodes and edges. The system processes numeric embeddings of the first node and the second node using a manifold neural network to generate respective manifold coordinates of the first node and the second node. The system applies a learned edge function to the manifold coordinates of the first node and the manifold coordinates of the second node to generate an edge score that represents a likelihood that an entity represented by the first node and an entity represented by the second node have a particular relationship.

    LEARNING COMPRESSIBLE FEATURES
    5.
    发明申请
    LEARNING COMPRESSIBLE FEATURES 审中-公开

    公开(公告)号：US20200311548A1

    公开(公告)日：2020-10-01

    申请号：US16666689

    申请日：2019-10-29

    申请人： Google LLC

    发明人： Abhinav Shrivastava ,  Saurabh Singh ,  Johannes Balle ,  Sami Ahmad Abu-El-Haija ,  Nicholas Johnston ,  George Dan Toderici

    IPC分类号： G06N3/08 ,  G06N3/063 ,  G06K9/62 ,  G06F17/15

    摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, by a neural network (NN), a dataset for generating features from the dataset. A first set of features is computed from the dataset using at least a feature layer of the NN. The first set of features i) is characterized by a measure of informativeness; and ii) is computed such that a size of the first set of features is compressible into a second set of features that is smaller in size than the first set of features and that has a same measure of informativeness as the measure of informativeness of the first set of features. The second set of features if generated from the first set of features using a compression method that compresses the first set of features to generate the second set of features.