ADAPTIVE ACQUISITION FOR COMPRESSED SENSING
    116.
    发明公开

    公开(公告)号:US20240248952A1

    公开(公告)日:2024-07-25

    申请号:US18475995

    申请日:2023-09-27

    CPC classification number: G06F17/16

    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for reinforcement-learning-based compressed sensing. An observed signal tensor comprising a plurality of elements is accessed, and a subset of elements of a sensing matrix is generated based on processing, from among the plurality of elements, a subset of elements of the observed signal tensor using an acquisition neural network. A subset of elements of a reconstructed signal tensor is generated based on processing a second subset of elements of the observed signal tensor and the subset of elements of the sensing matrix using a reconstruction neural network. At least the first subset of elements of the reconstructed signal tensor is output.

    EFFICIENT MACHINE LEARNING MODEL ARCHITECTURES FOR TRAINING AND INFERENCE

    公开(公告)号:US20240202529A1

    公开(公告)日:2024-06-20

    申请号:US18068987

    申请日:2022-12-20

    CPC classification number: G06N3/084

    Abstract: Certain aspects of the present disclosure provide techniques for improved machine learning. A data tensor is generated as output from a layer of a neural network. A first subset of the first data tensor and a second subset of the first data tensor are generated using a tensor splitting operation. The second subset of the first data tensor is stored, and the first subset of the first data tensor is provided to a subsequent layer of the neural network. One or more parameters of the layer of the neural network are refined based at least in part on the stored second subset of the first data tensor.

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