Computation of a saddle-point
    1.
    发明授权

    公开(公告)号:US11343650B1

    公开(公告)日:2022-05-24

    申请号:US17131753

    申请日:2020-12-23

    Abstract: An unconstrained saddle point of a function is obtained by computing a combination of a first subspace for minimization, and a second subspace for maximization. A combination of a current location including a first and second current location within the first and second subspace is iteratively selected. From the current location, a combination of a step-size including a first and second step-size along a first and second direction of the first and second subspace, is computed. The first and second step-size is to a next first and second location within the first and second subspace. The current location is set to a next location including the next first and second location. The combination of the first and second subspace is according to the next location. The iterations terminate when the next location meets a requirement denoting the unconstrained saddle point. The location indicating the unconstrained saddle point is provided.

    System and a method for error correction coding using a deep neural network

    公开(公告)号:US10735141B2

    公开(公告)日:2020-08-04

    申请号:US16229820

    申请日:2018-12-21

    Abstract: A system for reducing analog noise in a noisy channel, comprising: an interface configured to receive analog channel output comprising a stream of noisy binary codewords of a linear code; and a computation component configured to perform the following: for each analog segment of the analog channel output of block length: calculating an absolute value representation and a sign representation of a respective analog segment, calculating a multiplication of a binary representation of the sign representation with a parity matrix of the linear code, inputting the absolute value representation and the outcome of the multiplication into a neural network for acquiring a neural network output, and estimating a binary codeword by component-wise multiplication of the neural network output and the sign representation.

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