Patents by Inventor Abhijit S. Mudigonda

Abhijit S. Mudigonda 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).

  • Restructuring deep neural networks to reduce the number of parameters
    Patent number: 11663443
    Abstract: Techniques are described for reducing the number of parameters of a deep neural network model. According to one or more embodiments, a device can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a structure extraction component that determines a number of input nodes associated with a fully connected layer of a deep neural network model. The computer executable components can further comprise a transformation component that replaces the fully connected layer with a number of sparsely connected sublayers, wherein the sparsely connected sublayers have fewer connections than the fully connecter layer, and wherein the number of sparsely connected sublayers is determined based on a defined decrease to the number of input nodes.
    Type: Grant
    Filed: November 21, 2018
    Date of Patent: May 30, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Dan Gutfreund, Quanfu Fan, Abhijit S. Mudigonda
  • RESTRUCTURING DEEP NEURAL NETWORKS TO REDUCE THE NUMBER OF PARAMETERS
    Publication number: 20200160144
    Abstract: Techniques are described for reducing the number of parameters of a deep neural network model. According to one or more embodiments, a device can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a structure extraction component that determines a number of input nodes associated with a fully connected layer of a deep neural network model. The computer executable components can further comprise a transformation component that replaces the fully connected layer with a number of sparsely connected sublayers, wherein the sparsely connected sublayers have fewer connections than the fully connecter layer, and wherein the number of sparsely connected sublayers is determined based on a defined decrease to the number of input nodes.
    Type: Application
    Filed: November 21, 2018
    Publication date: May 21, 2020
    Inventors: Dan Gutfreund, Quanfu Fan, Abhijit S. Mudigonda