Epistemic and aleatoric deep plasticity based on sound feedback

    公开(公告)号:GB2584583A

    公开(公告)日:2020-12-09

    申请号:GB202015562

    申请日:2019-03-06

    Applicant: IBM

    Abstract: Simulating uncertainty in an artificial neural network is provided. Aleatoric uncertainty is simulated to measure what the artificial neural network does not understand from sensor data received from an object operating in a real-world environment by adding random values to edge weights between nodes in the artificial neural network during backpropagation of output data of the artificial neural network and measuring impact on the output data by the added random values to the edge weights between the nodes. Epistemic uncertainty is simulated to measure what the artificial neural network does not know by dropping out a selected node from each respective layer of the artificial neural network during forward propagation of the sensor data and measuring impact of dropped out nodes on the output data of the artificial neural network. An action corresponding to the object is performed based on the impact of simulating the aleatoric and epistemic uncertainty.

    Dynamic audiovisual segment padding for machine learning

    公开(公告)号:GB2596463A

    公开(公告)日:2021-12-29

    申请号:GB202113427

    申请日:2020-02-25

    Applicant: IBM

    Abstract: Techniques for padding audiovisual clips (for example, audiovisual clips of sporting events) for the purpose of causing the clip to have a predetermined duration so that the padded clip can be evaluated for viewer interest by a machine learning (ML) algorithm (310) are provided. The unpadded clip is padded with audiovisual segment(s) that will cause the padded clip to have a level of viewer interest that it would have if the unpadded clip had been longer. The padded segments are synthetic images generated by a generative adversarial network such that the synthetic images would have the same level of viewer interest (as adjudged by an ML algorithm (310)) as if the unpadded clip had been shot to be longer.

    Dynamically determining a region
    4.
    发明专利

    公开(公告)号:GB2590257A

    公开(公告)日:2021-06-23

    申请号:GB202100856

    申请日:2019-06-26

    Applicant: IBM

    Abstract: A computer-implemented method includes monitoring, by a computing device, sensor data during gameplay of a sporting event; determining, by the computing device, predictive factors associated with a target based on the monitoring the sensor data; determining, by the computing device, a real-time region of effectiveness for the target based on the predictive factors and training data identifying historical effectiveness of the target;and outputting, by the computing device, the real-time region of effectiveness for displaying the real-time region of effectiveness around the target.

    Speech recognition using data analysis and dilation of interlaced audio input

    公开(公告)号:GB2615421B

    公开(公告)日:2025-05-07

    申请号:GB202303909

    申请日:2021-08-24

    Applicant: IBM

    Abstract: The disclosure includes using dilation of speech content from an interlaced audio input for speech recognition. A learning model is initiated to determine dilation parameters for each of a plurality of audible sounds of speech content from a plurality of speakers received at a computer as an audio input. As part of the learning model, a change of each of a plurality of independent sounds is determined in response to an audio stimulus, the independent sounds being derived from the audio input. The disclosure applies the dilation parameters, respectively, based on the change of each of the independent sounds. A voice print is constructed for each of the speakers based on the independent sounds and the dilation parameters, respectively. Speech content is attributed to each of the plurality of speakers based at least in part on the voice print, respectively, and the independent sounds.

    Distributing computation workloads based on calculated compute gravity within differing computing paradigms

    公开(公告)号:GB2607477A

    公开(公告)日:2022-12-07

    申请号:GB202211239

    申请日:2020-12-18

    Applicant: IBM

    Abstract: Distributing computation workload among computing nodes of differing computing paradigms is provided. Compute gravity of each computing node in a cloud computing paradigm and each computing node in a client network computing paradigm within an Internet of Systems is calculated. Each component part of an algorithm is distributed to an appropriate computing node of the cloud computing paradigm and client network computing paradigm based on calculated compute gravity of each respective computing node within the Internet of Systems. Computation workload of each component part of the algorithm is distributed to a respective computing node of the cloud computing paradigm and the client network computing paradigm having a corresponding component part of the algorithm for processing.

    Dynamic audiovisual segment padding for machine learning

    公开(公告)号:GB2596463B

    公开(公告)日:2022-05-11

    申请号:GB202113427

    申请日:2020-02-25

    Applicant: IBM

    Abstract: Techniques for padding audiovisual clips (for example, audiovisual clips of sporting events) for the purpose of causing the clip to have a predetermined duration so that the padded clip can be evaluated for viewer interest by a machine learning (ML) algorithm. The unpadded clip is padded with audiovisual segment(s) that will cause the padded clip to have a level of viewer interest that it would have if the unpadded clip had been longer. In some embodiments the padded segments are synthetic images generated by a generative adversarial network such that the synthetic images would have the same level of viewer interest (as adjudged by an ML algorithm) as if the unpadded clip had been shot to be longer.

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