Patents by Inventor Dimitrios Sarigiannis

Dimitrios Sarigiannis 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).

  • Patent number: 11803779
    Abstract: In an approach for constructing an ensemble model from a set of base learners, a processor performs a plurality of boosting iterations, where: at each boosting iteration of the plurality of boosting iterations, a base learner is selected at random from a set of base learners, according to a sampling probability distribution of the set of base learners, and trained according to a training dataset; and the sampling probability distribution is altered: (i) after selecting a first base learner at a first boosting iteration of the plurality of boosting iterations and (ii) prior to selecting a second base learner at a final boosting iteration of the plurality of boosting iterations. A processor constructs an ensemble model based on base learners selected and trained during the plurality of boosting iterations.
    Type: Grant
    Filed: February 25, 2020
    Date of Patent: October 31, 2023
    Assignee: International Business Machines Corporation
    Inventors: Thomas Parnell, Andreea Anghel, Nikolas Ioannou, Nikolaos Papandreou, Celestine Mendler-Duenner, Dimitrios Sarigiannis, Charalampos Pozidis
  • Patent number: 11461694
    Abstract: Methods are provided for implementing training of a machine learning model in a processing system, together with systems for performing such methods. A method includes providing a core module for effecting a generic optimization process in the processing system, and in response to a selective input, defining a set of derivative modules, for effecting computation of first and second derivatives of selected functions ƒ and g in the processing system, to be used with the core module in the training operation. The method further comprises performing, in the processing system, the generic optimization process effected by the core module using derivative computations effected by the derivative modules.
    Type: Grant
    Filed: September 27, 2018
    Date of Patent: October 4, 2022
    Assignee: International Business Machines Corporation
    Inventors: Thomas Parnell, Celestine Duenner, Dimitrios Sarigiannis, Charalampos Pozidis
  • Patent number: 11315035
    Abstract: Computer-implemented methods are provided for implementing training of a machine learning model in a heterogeneous processing system comprising a host computer operatively interconnected with an accelerator unit. The training includes a stochastic optimization process for optimizing a function of a training data matrix X, having data elements Xi,j with row coordinates i=1 to n and column coordinates j=1 to m, and a model vector w having elements wj. For successive batches of the training data, defined by respective subsets of one of the row coordinates and column coordinates, random numbers associated with respective coordinates in a current batch b are generated in the host computer and sent to the accelerator unit. In parallel with generating the random numbers for batch b, batch b is copied from the host computer to the accelerator unit.
    Type: Grant
    Filed: December 10, 2018
    Date of Patent: April 26, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Thomas Parnell, Celestine Duenner, Charalampos Pozidis, Dimitrios Sarigiannis
  • Publication number: 20210264320
    Abstract: In an approach for constructing an ensemble model from a set of base learners, a processor performs a plurality of boosting iterations, where: at each boosting iteration of the plurality of boosting iterations, a base learner is selected at random from a set of base learners, according to a sampling probability distribution of the set of base learners, and trained according to a training dataset; and the sampling probability distribution is altered: (i) after selecting a first base learner at a first boosting iteration of the plurality of boosting iterations and (ii) prior to selecting a second base learner at a final boosting iteration of the plurality of boosting iterations. A processor constructs an ensemble model based on base learners selected and trained during the plurality of boosting iterations.
    Type: Application
    Filed: February 25, 2020
    Publication date: August 26, 2021
    Inventors: Thomas Parnell, Andreea Anghel, Nikolas loannou, Nikolaos Papandreou, Celestine Mendler-Duenner, Dimitrios Sarigiannis, Charalampos Pozidis
  • Publication number: 20200184369
    Abstract: Computer-implemented methods are provided for implementing training of a machine learning model in a heterogeneous processing system comprising a host computer operatively interconnected with an accelerator unit. The training includes a stochastic optimization process for optimizing a function of a training data matrix X, having data elements Xi,j with row coordinates i=1 to n and column coordinates j=1 to m, and a model vector w having elements wj. For successive batches of the training data, defined by respective subsets of one of the row coordinates and column coordinates, random numbers associated with respective coordinates in a current batch b are generated in the host computer and sent to the accelerator unit. In parallel with generating the random numbers for batch b, batch b is copied from the host computer to the accelerator unit.
    Type: Application
    Filed: December 10, 2018
    Publication date: June 11, 2020
    Inventors: Thomas Parnell, Celestine Duenner, Charalampos Pozidis, Dimitrios Sarigiannis
  • Publication number: 20200104276
    Abstract: Methods are provided for implementing training of a machine learning model in a processing system, together with systems for performing such methods. A method includes providing a core module for effecting a generic optimization process in the processing system, and in response to a selective input, defining a set of derivative modules, for effecting computation of first and second derivatives of selected functions ƒ and g in the processing system, to be used with the core module in the training operation. The method further comprises performing, in the processing system, the generic optimization process effected by the core module using derivative computations effected by the derivative modules.
    Type: Application
    Filed: September 27, 2018
    Publication date: April 2, 2020
    Inventors: Thomas Parnell, Celestine Duenner, Dimitrios Sarigiannis, Charalampos Pozidis