SYSTEM TO ANALYZE AND ENHANCE SOFTWARE BASED ON GRAPH ATTENTION NETWORKS

    公开(公告)号:US20200326934A1

    公开(公告)日:2020-10-15

    申请号:US16913756

    申请日:2020-06-26

    Abstract: Systems, apparatuses and methods may provide for technology that generates a dependence graph based on a plurality of intermediate representation (IR) code instructions associated with a compiled program code, generates a set of graph embedding vectors based on the plurality of IR code instructions, and determines, via a neural network, one of an analysis of the compiled program code or an enhancement of the program code based on the dependence graph and the set of graph embedding vectors. The technology may provide a graph attention neural network that includes a recurrent block and at least one task-specific neural network layer, the recurrent block including a graph attention layer and a transition function. The technology may also apply dynamic per-position recurrence-halting to determine a number of recurring steps for each position in the recurrent block based on adaptive computation time.

    TECHNOLOGY TO HANDLE AMBIGUITY IN AUTOMATED CONTROL SYSTEMS

    公开(公告)号:US20200326696A1

    公开(公告)日:2020-10-15

    申请号:US16913845

    申请日:2020-06-26

    Abstract: Systems, apparatuses and methods may provide for technology that obtains categorization information and corresponding uncertainty information from a perception subsystem, wherein the categorization information and the corresponding uncertainty information are to be associated with an object in an environment. The technology may also determine whether the corresponding uncertainty information satisfies one or more relevance criteria, and automatically control the perception subsystem to increase an accuracy in one or more subsequent categorizations of the object if the corresponding uncertainty information satisfies the one or more relevance criteria. In one example, determining whether the corresponding uncertainty information satisfies the relevance criteria includes taking a plurality of samples from the categorization information and the corresponding uncertainty information, generating a plurality of actuation plans based on the plurality of samples, and determining a safety deviation across the plurality of actuation plans, wherein the relevance criteria are satisfied if the safety deviation exceeds a threshold.

    System to analyze and enhance software based on graph attention networks

    公开(公告)号:US11640295B2

    公开(公告)日:2023-05-02

    申请号:US16913756

    申请日:2020-06-26

    Abstract: Systems, apparatuses and methods may provide for technology that generates a dependence graph based on a plurality of intermediate representation (IR) code instructions associated with a compiled program code, generates a set of graph embedding vectors based on the plurality of IR code instructions, and determines, via a neural network, one of an analysis of the compiled program code or an enhancement of the program code based on the dependence graph and the set of graph embedding vectors. The technology may provide a graph attention neural network that includes a recurrent block and at least one task-specific neural network layer, the recurrent block including a graph attention layer and a transition function. The technology may also apply dynamic per-position recurrence-halting to determine a number of recurring steps for each position in the recurrent block based on adaptive computation time.

    Technology to handle ambiguity in automated control systems

    公开(公告)号:US11493914B2

    公开(公告)日:2022-11-08

    申请号:US16913845

    申请日:2020-06-26

    Abstract: Systems, apparatuses and methods may provide for technology that obtains categorization information and corresponding uncertainty information from a perception subsystem, wherein the categorization information and the corresponding uncertainty information are to be associated with an object in an environment. The technology may also determine whether the corresponding uncertainty information satisfies one or more relevance criteria, and automatically control the perception subsystem to increase an accuracy in one or more subsequent categorizations of the object if the corresponding uncertainty information satisfies the one or more relevance criteria. In one example, determining whether the corresponding uncertainty information satisfies the relevance criteria includes taking a plurality of samples from the categorization information and the corresponding uncertainty information, generating a plurality of actuation plans based on the plurality of samples, and determining a safety deviation across the plurality of actuation plans, wherein the relevance criteria are satisfied if the safety deviation exceeds a threshold.

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