CONSTRAINED MASKING FOR SPARSIFICATION IN MACHINE LEARNING

    公开(公告)号:US20240095504A1

    公开(公告)日:2024-03-21

    申请号:US17932941

    申请日:2022-09-16

    CPC classification number: G06N3/0481 G06N3/063 G06N3/08 G06N5/04

    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for feature masking. A feature tensor is accessed in a neural network, and a feature mask is generated by processing the feature tensor using a masking subnetwork, where the masking subnetwork was trained based at least in part on a polarization constraint and an activation constraint to generate feature masks. A masked feature tensor is generated based on the feature tensor and the feature mask, and an output inference is generated using the neural network based at least in part on the masked feature tensor.

    DESPARSIFIED CONVOLUTION FOR SPARSE TENSORS
    22.
    发明公开

    公开(公告)号:US20240095493A1

    公开(公告)日:2024-03-21

    申请号:US17932527

    申请日:2022-09-15

    CPC classification number: G06N3/04 G06F7/50 G06F7/523

    Abstract: Certain aspects of the present disclosure provide techniques for desparsified convolution. A weight tensor having unstructured sparsity is accessed, and a densified weight tensor is generated based on the weight tensor by directionally squeezing the weight tensor to remove sparse values, and generating a sparsity map based on the directional squeezing. The densified weight tensor and sparsity map are output for use in a convolutional neural network.

    VOLUMETRIC SAMPLING WITH CORRELATIVE CHARACTERIZATION FOR DENSE ESTIMATION

    公开(公告)号:US20230222673A1

    公开(公告)日:2023-07-13

    申请号:US18180730

    申请日:2023-03-08

    CPC classification number: G06T7/248 G06F18/24 G06T7/74 G06T2207/10016

    Abstract: Systems and techniques are described herein for performing optical flow estimation for one or more frames. For example, a process can include determining an optical flow prediction associated with a plurality of frames. The process can include determining a position of at least one feature associated with a first frame and determining, based on the position of the at least one feature in the first frame and the optical flow prediction, a position estimate of a search area for searching for the at least one feature in a second frame. The process can include determining, from within the search area, a position of the at least one feature in the second frame

    PARAMETERIZED ACTIVATION FUNCTIONS TO ADJUST MODEL LINEARITY

    公开(公告)号:US20230057454A1

    公开(公告)日:2023-02-23

    申请号:US17407085

    申请日:2021-08-19

    Abstract: Certain aspects of the present disclosure provide techniques for parameterized activation functions. Input data is processed with at least one layer of the neural network model comprising a parameterized activation function, and at least one trainable parameter of the parameterized activation function is updated based at least in part on output from the at least one layer of the neural network model. The at least one trainable parameter may adjust at least one of a range over which the parameterized activation function is nonlinear or a shape of the parameterized activation function, and/or may adjust a location of at least one pivot of the parameterized activation function.

    VOLUMETRIC SAMPLING WITH CORRELATIVE CHARACTERIZATION FOR DENSE ESTIMATION

    公开(公告)号:US20220398747A1

    公开(公告)日:2022-12-15

    申请号:US17344283

    申请日:2021-06-10

    Abstract: Systems and techniques are described herein for performing optical flow estimation for one or more frames. For example, a process can include determining an optical flow prediction associated with a plurality of frames. The process can include determining a position of at least one feature associated with a first frame and determining, based on the position of the at least one feature in the first frame and the optical flow prediction, a position estimate of a search area for searching for the at least one feature in a second frame. The process can include determining, from within the search area, a position of the at least one feature in the second frame

    ELASTIC BOTTLENECK ARCHITECTURES FOR VARIABLE CONVOLUTION OPERATIONS

    公开(公告)号:US20220019873A1

    公开(公告)日:2022-01-20

    申请号:US17379833

    申请日:2021-07-19

    Abstract: In one aspect of the present disclosure, a method includes: determining a number of loops for a convolution layer of an elastic bottleneck block; for each loop of the number of loops: loading a loop-specific set of convolution weights; performing a convolution operation using the loop-specific set of convolution-weights; and storing loop-specific convolution results in a local memory; and determining an output of the convolution layer based on a summation of loop-specific convolution results associated with each loop of the number of loops.

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