Patents by Inventor Jason McGhee

Jason McGhee has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20230004796
    Abstract: Systems and methods are described for developing and using neural network models. An example method of training a neural network includes: oscillating a learning rate while performing a preliminary training of a neural network; determining, based on the preliminary training, a number of training epochs to perform for a subsequent training session, and training the neural network using the determined number of training epochs. The systems and methods can be used to build neural network models that efficiently and accurately handle heterogeneous data.
    Type: Application
    Filed: May 13, 2022
    Publication date: January 5, 2023
    Applicant: DataRobot, Inc.
    Inventors: Zachary Albert Mayer, Jason McGhee, Jesse Bannon, Joshua Matthew Weiner
  • Patent number: 11334795
    Abstract: Systems and methods are described for developing and using neural network models. An example method of training a neural network includes: oscillating a learning rate while performing a preliminary training of a neural network; determining, based on the preliminary training, a number of training epochs to perform for a subsequent training session; and training the neural network using the determined number of training epochs. The systems and methods can be used to build neural network models that efficiently and accurately handle heterogeneous data.
    Type: Grant
    Filed: March 11, 2021
    Date of Patent: May 17, 2022
    Assignee: DataRobot, Inc.
    Inventors: Zachary Albert Mayer, Jason McGhee, Jesse Bannon, Joshua Matthew Weiner
  • Publication number: 20210287089
    Abstract: Systems and methods are described for developing and using neural network models. An example method of training a neural network includes: oscillating a learning rate while performing a preliminary training of a neural network; determining, based on the preliminary training, a number of training epochs to perform for a subsequent training session; and training the neural network using the determined number of training epochs. The systems and methods can be used to build neural network models that efficiently and accurately handle heterogeneous data.
    Type: Application
    Filed: March 11, 2021
    Publication date: September 16, 2021
    Inventors: Zachary Albert Mayer, Jason McGhee, Jesse Bannon, Joshua Matthew Weiner