Mixed Machine Learning Architecture
    1.
    发明申请

    公开(公告)号:US20190073581A1

    公开(公告)日:2019-03-07

    申请号:US15694707

    申请日:2017-09-01

    Applicant: Facebook, Inc.

    Abstract: A preprocessing module of a neural network has a first input and second input. The module generates multiple, different first latent vector representations of its first input, and multiple, different second latent vector representations of its second input. The module then models pairwise interactions between every unique pairwise combination of the first and second latent vector representations. The module then produces an intermediate output by combining the results of the modeled pairwise interactions.

    Mixed machine learning architecture

    公开(公告)号:US11144812B2

    公开(公告)日:2021-10-12

    申请号:US15694707

    申请日:2017-09-01

    Applicant: Facebook, Inc.

    Abstract: A preprocessing module of a neural network has a first input and second input. The module generates multiple, different first latent vector representations of its first input, and multiple, different second latent vector representations of its second input. The module then models pairwise interactions between every unique pairwise combination of the first and second latent vector representations. The module then produces an intermediate output by combining the results of the modeled pairwise interactions.

    Nested machine learning architecture

    公开(公告)号:US11132604B2

    公开(公告)日:2021-09-28

    申请号:US15694695

    申请日:2017-09-01

    Applicant: Facebook, Inc.

    Abstract: In one embodiment, a method includes a preprocessing stage of a neural network model, where the preprocessing stage includes first and second preprocessing modules. Each of the two modules has first input that may receive a dense input and a second input that may receive a sparse input. Each module generates latent vector representations of their respective first and second inputs, and combine the latent vectors with the original first input to define an intermediate output. The intermediate output of the first module is fed into the first input of the second module.

    Nested Machine Learning Architecture
    4.
    发明申请

    公开(公告)号:US20190073586A1

    公开(公告)日:2019-03-07

    申请号:US15694695

    申请日:2017-09-01

    Applicant: Facebook, Inc.

    Abstract: In one embodiment, a method includes a preprocessing stage of a neural network model, where the preprocessing stage includes first and second preprocessing modules. Each of the two modules has first input that may receive a dense input and a second input that may receive a sparse input. Each module generates latent vector representations of their respective first and second inputs, and combine the latent vectors with the original first input to define an intermediate output. The intermediate output of the first module is fed into the first input of the second module.

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