Patents by Inventor Prateek Katiyar

Prateek Katiyar 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: 20220284289
    Abstract: Computer-implemented method for determining an output signal based on an input signal and by means of a neural network. The neural network determines the output signal based on a layer output determined by a first layer of the neural network. The layer output is determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer.
    Type: Application
    Filed: February 23, 2022
    Publication date: September 8, 2022
    Inventors: Claudia Blaiotta, Prateek Katiyar
  • Publication number: 20220076119
    Abstract: A device and a method of training a generative neural network. The method includes: generating an edge image using an edge detection applied to a digital image, the edge image comprising a plurality of edge pixels determined as representing edges of one or more digital objects in the digital image; selecting edge-pixels from the plurality of edge pixels; providing a segmentation image using the digital image, the segmentation image comprising a plurality of first pixels, the positions of the first pixels corresponding to the positions of the selected edge-pixels; selecting one or more second pixels for each first pixel in the segmentation image; generating a distorted segmentation image using a two-dimensional distortion applied to the segmentation image; and training the generative neural network using the distorted segmentation image as input image to estimate the digital image.
    Type: Application
    Filed: September 1, 2021
    Publication date: March 10, 2022
    Inventors: Anna Khoreva, Prateek Katiyar
  • Publication number: 20220036181
    Abstract: A computer-implemented method for training a neural network including a neural ordinary differential equation (ODE) block. A first ODE solver may be used to train the neural ODE block. A second ODE solver may be used to train and verify that the neural ODE block describes an ODE as an ODE flow. During a forward pass of an iteration of training, a first performance value is obtained by applying the first ODE solver to the neural ODE block and a second performance value is obtained by applying the second ODE solver to the neural ODE block. An accuracy parameter of the first ODE solver is adjusted based on the difference between the first performance value and the second performance value.
    Type: Application
    Filed: June 18, 2021
    Publication date: February 3, 2022
    Inventors: Katharina Ott, Michael Tiemann, Prateek Katiyar
  • Publication number: 20210019620
    Abstract: A method for operating a neural network is described comprising determining, for neural network input sensor data, neural network output data using the neural network, selecting a portion of output data points to form a region of interest and determining, for each of at least some output data points outside the region of interest, a contribution value representing a contribution of one or more input data points associated with the output data point for the neural network determining the output data point values assigned to output data points in the region of interest.
    Type: Application
    Filed: July 7, 2020
    Publication date: January 21, 2021
    Inventors: Andres Mauricio Munoz Delgado, Anna Khoreva, Lukas Hoyer, Prateek Katiyar, Volker Fischer