中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

13 records (took 0.025 seconds)

    OPTIMIZATION USING A PROBABILISTIC FRAMEWORK FOR TIME SERIES DATA AND STOCHASTIC EVENT DATA
    8.
    发明公开
    OPTIMIZATION USING A PROBABILISTIC FRAMEWORK FOR TIME SERIES DATA AND STOCHASTIC EVENT DATA 审中-公开

    公开(公告)号：US20240153007A1

    公开(公告)日：2024-05-09

    申请号：US17978486

    申请日：2022-11-01

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Yada Zhu ,  Yang Zhang ,  Tian Gao ,  David Cox

    IPC: G06Q40/06

    CPC classification number: G06Q40/06

    Abstract: A method includes: creating a training data set based on user input, the training data set including time series data of a price of an asset and stochastic event data of events related to the asset; creating an event intensity model that models an event intensity parameter of one of the events related to the asset, wherein the event intensity model comprises a proximal graphical event model (PGEM), and the creating the event intensity model includes learning parameters of the PGEM using machine learning and the training data set; creating a probabilistic time series model that predicts a probability distribution of a return of the asset, wherein the creating the probabilistic time series model includes learning parameters of the probabilistic time series model using machine learning and the training data set; and predicting a future return of the asset for a future time period using the probabilistic time series model.

    SIMULATOR-ASSISTED TRAINING FOR INTERPRETABLE GENERATIVE MODELS
    9.
    发明申请
    SIMULATOR-ASSISTED TRAINING FOR INTERPRETABLE GENERATIVE MODELS 有权

    公开(公告)号：US20210133539A1

    公开(公告)日：2021-05-06

    申请号：US16672996

    申请日：2019-11-04

    Applicant: International Business Machines Corporation

    Inventor： Akash Srivastava ,  Jessie Carrigan Rosenberg ,  Dan Gutfreund ,  David Cox

    IPC: G06N3/04 ,  G06N3/08 ,  G06N5/04

    Abstract: A generator network of a variational autoencoder can be trained to approximate a simulator and generate a first result. The simulator is associated with input data, based on which the simulator outputs output data. A training data set for the generator network can include the simulator's input data and output data. Based on the simulator's output data and the first result of the generator network, an inference network of the variational autoencoder can be trained to generate a second result. The second result of the trained inference network inverts the first result of the generator and approximates the simulator's input data. The trained inference network can function as an inverted simulator.

Patent Agency Ranking