Patents by Inventor Praneeth Karimireddy

Praneeth Karimireddy 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: 12001509
    Abstract: Generally, the present disclosure is directed to systems and methods that perform adaptive optimization with improved convergence properties. The adaptive optimization techniques described herein are useful in various optimization scenarios, including, for example, training a machine-learned model such as, for example, a neural network. In particular, according to one aspect of the present disclosure, a system implementing the adaptive optimization technique can, over a plurality of iterations, employ an adaptive per coordinate clipping threshold to clip a current first moment of the coordinate to obtain a current update value that enables faster convergence for the machine-learned model when the noise in the stochastic gradients is heavy tailed.
    Type: Grant
    Filed: March 17, 2020
    Date of Patent: June 4, 2024
    Assignee: GOOGLE LLC
    Inventors: Seungyeon Kim, Jingzhao Zhang, Andreas Veit, Sanjiv Kumar, Sashank Reddi, Praneeth Karimireddy
  • Publication number: 20210295201
    Abstract: Generally, the present disclosure is directed to systems and methods that perform adaptive optimization with improved convergence properties. The adaptive optimization techniques described herein are useful in various optimization scenarios, including, for example, training a machine-learned model such as, for example, a neural network. In particular, according to one aspect of the present disclosure, a system implementing the adaptive optimization technique can, over a plurality of iterations, employ an adaptive per coordinate clipping threshold to clip a current first moment of the coordinate to obtain a current update value that enables faster convergence for the machine-learned model when the noise in the stochastic gradients is heavy tailed.
    Type: Application
    Filed: March 17, 2020
    Publication date: September 23, 2021
    Inventors: Seungyeon Kim, Jingzhao Zhang, Andreas Veit, Sanjiv Kumar, Sashank Reddi, Praneeth Karimireddy