Patents by Inventor Eric O. Korman

Eric O. Korman 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).

  • VARIABLE PARAMETER PROBABILITY FOR MACHINE-LEARNING MODEL GENERATION AND TRAINING
    Publication number: 20210390416
    Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.
    Type: Application
    Filed: August 27, 2021
    Publication date: December 16, 2021
    Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Eric O. Korman
  • Execution of a genetic algorithm with variable evolutionary weights of topological parameters for neural network generation and training
    Patent number: 11106978
    Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.
    Type: Grant
    Filed: September 8, 2017
    Date of Patent: August 31, 2021
    Assignee: SPARKCOGNITION, INC.
    Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Eric O. Korman
  • EXECUTION OF A GENETIC ALGORITHM WITH VARIABLE EVOLUTIONARY WEIGHTS OF TOPOLOGICAL PARAMETERS FOR NEURAL NETWORK GENERATION AND TRAINING
    Publication number: 20190080240
    Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.
    Type: Application
    Filed: September 8, 2017
    Publication date: March 14, 2019
    Inventors: Sari Andoni, Keith D. Moore, Elmira M. Bonab, Junhwan Choi, Eric O. Korman