SYSTEM AND METHOD FOR DEEP MEMORY NETWORK
    2.
    发明申请

    公开(公告)号:US20200050934A1

    公开(公告)日:2020-02-13

    申请号:US16535380

    申请日:2019-08-08

    Abstract: An electronic device including a deep memory model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive input data to the deep memory model. The at least one processor is also configured to extract a history state of an external memory coupled to the deep memory model based on the input data. The at least one processor is further configured to update the history state of the external memory based on the input data. In addition, the at least one processor is configured to output a prediction based on the extracted history state of the external memory.

    SYSTEM AND METHOD FOR ACTIVE MACHINE LEARNING

    公开(公告)号:US20190318261A1

    公开(公告)日:2019-10-17

    申请号:US16370542

    申请日:2019-03-29

    Abstract: An electronic device for active learning includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to select one or more entries from a data set including unlabeled data based on a similarity between the one or more entries and labeled data. The at least one processor is further configured to cause the one or more entries to be labeled.

    System and method for deep memory network

    公开(公告)号:US11775815B2

    公开(公告)日:2023-10-03

    申请号:US16535380

    申请日:2019-08-08

    CPC classification number: G06N3/08 G06N5/04

    Abstract: An electronic device including a deep memory model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive input data to the deep memory model. The at least one processor is also configured to extract a history state of an external memory coupled to the deep memory model based on the input data. The at least one processor is further configured to update the history state of the external memory based on the input data. In addition, the at least one processor is configured to output a prediction based on the extracted history state of the external memory.

    MULTI-MODEL STRUCTURES FOR CLASSIFICATION AND INTENT DETERMINATION

    公开(公告)号:US20200334539A1

    公开(公告)日:2020-10-22

    申请号:US16728987

    申请日:2019-12-27

    Abstract: Intent determination based on one or more multi-model structures can include generating an output from each of a plurality of domain-specific models in response to a received input. The domain-specific models can comprise simultaneously trained machine learning models that are trained using a corresponding local loss metric for each domain-specific model and a global loss metric for the plurality of domain-specific models. The presence or absence of an intent corresponding to one or more domain-specific models can be determined by classifying the output of each domain-specific model.

    CONTROLLABLE AND INTERPRETABLE CONTENT CONVERSION

    公开(公告)号:US20200257962A1

    公开(公告)日:2020-08-13

    申请号:US16273973

    申请日:2019-02-12

    Abstract: Systems and methods are described for converting input content. A first model may convert input content to an output content that exhibits one or more desired properties. A second model may determine if the conversion meets a desired quality of conversion using a discriminating function. The discriminating function may determine a difference between properties of the output content and properties of desired content, where the difference corresponds to the success of the conversion applying the desired properties. Updated control data may be generated by a third model using information from the second model, where the updated control data may be used by the first model to reduce the determined difference. After updated control data has been generated, the foregoing steps may be repeated based upon the updated control data. One of a plurality of different actions may be determined in response to the difference.

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