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    MODELING TROPICAL CYCLONE SURFACE FIELDS FOR IMPACT ASSESSMENT
    2.
    发明专利
    MODELING TROPICAL CYCLONE SURFACE FIELDS FOR IMPACT ASSESSMENT 未知

    公开(公告)号：ZA202106185B

    公开(公告)日：2024-11-27

    申请号：ZA202106185

    申请日：2021-08-26

    Applicant: IBM

    Inventor： WATSON CAMPBELL D ,  VOS ETIENNE EBEN ,  WELDEMARIAM KOMMINIST

    Abstract: Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

    OPTIMIZING CAPACITY AND LEARNING OF WEIGHTED REAL-VALUED LOGIC
    3.
    发明专利
    OPTIMIZING CAPACITY AND LEARNING OF WEIGHTED REAL-VALUED LOGIC 未知

    公开(公告)号：ZA202103206B

    公开(公告)日：2022-08-31

    申请号：ZA202103206

    申请日：2021-05-12

    Applicant: IBM

    Inventor： LUUS FRANCOIS PIERRE ,  RIEGEL RYAN ,  AKHALWAYA ISMAIL YUNUS ,  KHAN NAWEED AGHMAD ,  VOS ETIENNE EBEN ,  MAKONDO NDIVHUWO

    Abstract: Maximum expressivity can be received representing a ratio between maximum and minimum input weights to a neuron of a neural network implementing a weighted real-valued logic gate. Operator arity can be received associated with the neuron. Logical constraints associated with the weighted real-valued logic gate can be determined in terms of weights associated with inputs to the neuron, a threshold-of-truth, and a neuron threshold for activation. The threshold-of-truth can be determined as a parameter used in an activation function of the neuron, based on solving an activation optimization formulated based on the logical constraints, the activation optimization maximizing a product of expressivity representing a distribution width of input weights to the neuron and gradient quality for the neuron given the operator arity and the maximum expressivity. The neural network of logical neurons can be trained using the activation function at the neuron, the activation function using the determined threshold-of-truth.

    GENERATIVE ONTOLOGY LEARNING AND NATURAL LANGUAGE PROCESSING WITH PREDICTIVE LANGUAGE MODELS
    5.
    发明专利
    GENERATIVE ONTOLOGY LEARNING AND NATURAL LANGUAGE PROCESSING WITH PREDICTIVE LANGUAGE MODELS 未知

    公开(公告)号：ZA202102725B

    公开(公告)日：2022-08-31

    申请号：ZA202102725

    申请日：2021-04-23

    Applicant: IBM

    Inventor： LUUS FRANCOIS PIERRE ,  VOS ETIENNE EBEN ,  MAKONDO NDIVHUWO ,  KHAN NAWEED AGHMAD ,  AKHALWAYA ISMAIL YUNUS

    Abstract: An ontology topic is selected and a pretrained predictive language model is primed to create a predictive primed model based on one or more ontological rules corresponding to the selected ontology topic. Using the predictive primed model, natural language text is generated based on the ontology topic and guidance of a prediction steering component. The predictive primed model is guided in selecting text that is predicted to be appropriate for the ontology topic and the generated natural language text. The generated natural language text is processed to generate extracted ontology rules and the extracted ontology rules are compared to one or more rules of an ontology rule database that correspond to the ontology topic. A check is performed to determine if a performance of the ontology extractor is acceptable.

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