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    Generating and managing deep tensor neural networks
    6.
    发明授权
    Generating and managing deep tensor neural networks 有权

    公开(公告)号：US11531902B2

    公开(公告)日：2022-12-20

    申请号：US16189744

    申请日：2018-11-13

    申请人： International Business Machines Corporation ,  Trustees of Tufts College ,  Ramot at Tel-Aviv University Ltd.

    发明人： Lior Horesh ,  Elizabeth Newman ,  Misha E. Kilmer ,  Haim Avron

    IPC分类号： G06N3/12 ,  G06N3/08 ,  G06N3/04

    摘要： Techniques for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update. NMC updates the network, including weight and bias parameters, based on the update, by application of tensor-tensor operations.