Patents by Inventor Xiaoliang Dai

Xiaoliang Dai 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: 11521068
    Abstract: According to various embodiments, a method for generating one or more optimal neural network architectures is disclosed. The method includes providing an initial seed neural network architecture and utilizing sequential phases to synthesize the neural network until a desired neural network architecture is reached. The phases include a gradient-based growth phase and a magnitude-based pruning phase.
    Type: Grant
    Filed: October 25, 2018
    Date of Patent: December 6, 2022
    Assignee: THE TRUSTEES OF PRINCETON UNIVERSITY
    Inventors: Xiaoliang Dai, Hongxu Yin, Niraj K. Jha
  • Publication number: 20220240864
    Abstract: According to various embodiments, a machine-learning based system for diabetes analysis is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs and demographic data from a user interface. The processors are further configured to train at least one neural network based on a grow-and-prune paradigm to generate at least one diabetes inference model. The neural network grows at least one of connections and neurons based on gradient information and prunes away at least one of connections and neurons based on magnitude information. The processors are also configured to output a diabetes-based decision by inputting the received physiological data and demographic data into the generated diabetes inference model.
    Type: Application
    Filed: June 16, 2020
    Publication date: August 4, 2022
    Applicant: The Trustees of Princeton University
    Inventors: Hongxu Yin, Bilal Mukadam, Xiaoliang Dai, Niraj K. Jha
  • Publication number: 20220222534
    Abstract: According to various embodiments, a method for generating a compact and accurate neural network for a dataset that has initial data and is updated with new data is disclosed. The method includes performing a first training on the initial neural network architecture to create a first trained neural network architecture. The method additionally includes performing a second training on the first trained neural network architecture when the dataset is updated with new data to create a second trained neural network architecture. The second training includes growing one or more connections for the new data based on a gradient of each connection, growing one or more connections for the new data and the initial data based on a gradient of each connection, and iteratively pruning one or more connections based on a magnitude of each connection until a desired neural network architecture is achieved.
    Type: Application
    Filed: March 20, 2020
    Publication date: July 14, 2022
    Applicant: The Trustees of Princeton University
    Inventors: Xiaoliang DAI, Hongxu YIN, Niraj K. JHA
  • Publication number: 20210182683
    Abstract: According to various embodiments, a method for generating one or more optimal neural network architectures is disclosed. The method includes providing an initial seed neural network architecture and utilizing sequential phases to synthesize the neural network until a desired neural network architecture is reached. The phases include a gradient-based growth phase and a magnitude-based pruning phase.
    Type: Application
    Filed: October 25, 2018
    Publication date: June 17, 2021
    Applicant: The Trustees of Princeton University
    Inventors: Xiaoliang DAI, Hongxu YIN, Niraj K. JHA
  • Publication number: 20210133540
    Abstract: According to various embodiments, a method for generating an optimal hidden-layer long short-term memory (H-LSTM) architecture is disclosed. The H-LSTM architecture includes a memory cell and a plurality of deep neural network (DNN) control gates enhanced with hidden layers. The method includes providing an initial seed H-LSTM architecture, training the initial seed H-LSTM architecture by growing one or more connections based on gradient information and iteratively pruning one or more connections based on magnitude information, and terminating the iterative pruning when training cannot achieve a predefined accuracy threshold.
    Type: Application
    Filed: March 14, 2019
    Publication date: May 6, 2021
    Applicant: The Trustees of Princeton University
    Inventors: Xiaoliang DAI, Hongxu YIN, Niraj K. JHA
  • Patent number: 10798238
    Abstract: According to various embodiments, a method for locating the user of a mobile device without accessing global position system (GPS) data is disclosed. The method includes determining the last location that the user was connected to a wireless network. The method further includes compiling publicly-available auxiliary information related to the last location. The method additionally includes classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking. The method also includes estimating the location of the user based on sensory and non-sensory data of the mobile device particular to the activity classification of the user.
    Type: Grant
    Filed: October 13, 2017
    Date of Patent: October 6, 2020
    Assignee: THE TRUSTEES OF PRINCETON UNIVERSITY
    Inventors: Arsalan Mosenia, Xiaoliang Dai, Prateek Mittal, Niraj K. Jha
  • Publication number: 20190289125
    Abstract: According to various embodiments, a method for locating the user of a mobile device without accessing global position system (GPS) data is disclosed. The method includes determining the last location that the user was connected to a wireless network. The method further includes compiling publicly-available auxiliary information related to the last location. The method additionally includes classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking. The method also includes estimating the location of the user based on sensory and non-sensory data of the mobile device particular to the activity classification of the user.
    Type: Application
    Filed: October 13, 2017
    Publication date: September 19, 2019
    Applicant: The Trustees of Princeton University
    Inventors: Arsalan Mosenia, Xiaoliang Dai, Prateek Mittal, Niraj K. Jha