Patents by Inventor MUSTAFA ONUR KABUL

MUSTAFA ONUR KABUL 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: 10360500
    Abstract: A computing system provides distributed training of a neural network model. Explore phase options, exploit phase options, a subset of a training dataset, and a validation dataset are distributed to a plurality of computing devices. (a) Execution of the model by the computing devices is requested using the subset stored at each computing device. (b) A first result of the execution is received from a computing device. (c) Next configuration data for the neural network model is selected based on the first result and distributed to the computing device. (a) to (c) is repeated until an exploration phase is complete. (d) Execution of the neural network model is requested. (e) A second result is received. (f) Next configuration data is computed based on the second result and distributed to the computing device. (d) to (f) is repeated until an exploitation phase is complete. The next configuration data defines the model.
    Type: Grant
    Filed: April 5, 2018
    Date of Patent: July 23, 2019
    Assignee: SAS Institute Inc.
    Inventors: Mustafa Onur Kabul, Lawrence E. Lewis
  • Patent number: 10192001
    Abstract: Convolutional neural networks can be visualized. For example, a graphical user interface (GUI) can include a matrix of symbols indicating feature-map values that represent a likelihood of a particular feature being present or absent in an input to a convolutional neural network. The GUI can also include a node-link diagram representing a feed forward neural network that forms part of the convolutional neural network. The node-link diagram can include a first row of symbols representing an input layer to the feed forward neural network, a second row of symbols representing a hidden layer of the feed forward neural network, and a third row of symbols representing an output layer of the feed forward neural network. Lines between the rows of symbols can represent connections between nodes in the input layer, the hidden layer, and the output layer of the feed forward neural network.
    Type: Grant
    Filed: October 4, 2017
    Date of Patent: January 29, 2019
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis, Mustafa Onur Kabul
  • Publication number: 20180307986
    Abstract: A computing system provides distributed training of a neural network model. Explore phase options, exploit phase options, a subset of a training dataset, and a validation dataset are distributed to a plurality of computing devices. (a) Execution of the model by the computing devices is requested using the subset stored at each computing device. (b) A first result of the execution is received from a computing device. (c) Next configuration data for the neural network model is selected based on the first result and distributed to the computing device. (a) to (c) is repeated until an exploration phase is complete. (d) Execution of the neural network model is requested. (e) A second result is received. (f) Next configuration data is computed based on the second result and distributed to the computing device. (d) to (f) is repeated until an exploitation phase is complete. The next configuration data defines the model.
    Type: Application
    Filed: April 5, 2018
    Publication date: October 25, 2018
    Inventors: Mustafa Onur Kabul, Lawrence E. Lewis
  • Patent number: 10048826
    Abstract: Interactive visualizations of a convolutional neural network are provided. For example, a graphical user interface (GUI) can include a matrix having symbols indicating feature-map values that represent likelihoods of particular features being present or absent at various locations in an input to a convolutional neural network. Each column in the matrix can have feature-map values generated by convolving the input to the convolutional neural network with a respective filter for identifying a particular feature in the input. The GUI can detect, via an input device, an interaction indicating that that the columns in the matrix are to be combined into a particular number of groups. Based on the interaction, the columns can be clustered into the particular number of groups using a clustering method. The matrix in the GUI can then be updated to visually represent each respective group of columns as a single column of symbols within the matrix.
    Type: Grant
    Filed: October 3, 2017
    Date of Patent: August 14, 2018
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis, Mustafa Onur Kabul
  • Publication number: 20180096078
    Abstract: Convolutional neural networks can be visualized. For example, a graphical user interface (GUI) can include a matrix of symbols indicating feature-map values that represent a likelihood of a particular feature being present or absent in an input to a convolutional neural network. The GUI can also include a node-link diagram representing a feed forward neural network that forms part of the convolutional neural network. The node-link diagram can include a first row of symbols representing an input layer to the feed forward neural network, a second row of symbols representing a hidden layer of the feed forward neural network, and a third row of symbols representing an output layer of the feed forward neural network. Lines between the rows of symbols can represent connections between nodes in the input layer, the hidden layer, and the output layer of the feed forward neural network.
    Type: Application
    Filed: October 4, 2017
    Publication date: April 5, 2018
    Applicants: SAS Institute Inc., North Carolina State University
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis, Mustafa Onur Kabul
  • Publication number: 20180095632
    Abstract: Interactive visualizations of a convolutional neural network are provided. For example, a graphical user interface (GUI) can include a matrix having symbols indicating feature-map values that represent likelihoods of particular features being present or absent at various locations in an input to a convolutional neural network. Each column in the matrix can have feature-map values generated by convolving the input to the convolutional neural network with a respective filter for identifying a particular feature in the input. The GUI can detect, via an input device, an interaction indicating that that the columns in the matrix are to be combined into a particular number of groups. Based on the interaction, the columns can be clustered into the particular number of groups using a clustering method. The matrix in the GUI can then be updated to visually represent each respective group of columns as a single column of symbols within the matrix.
    Type: Application
    Filed: October 3, 2017
    Publication date: April 5, 2018
    Applicants: SAS Institute Inc., North Carolina State University
    Inventors: SAMUEL PAUL LEEMAN-MUNK, SARATENDU SETHI, CHRISTOPHER GRAHAM HEALEY, SHAOLIANG NIE, KALPESH PADIA, RAVINDER DEVARAJAN, DAVID JAMES CAIRA, JORDAN RILEY BENSON, JAMES ALLEN COX, LAWRENCE E. LEWIS, MUSTAFA ONUR KABUL