Patents by Inventor Pei-Lun Liao

Pei-Lun Liao 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).

  • Two-stage training with non-randomized and randomized data
    Patent number: 11204973
    Abstract: In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.
    Type: Grant
    Filed: June 21, 2019
    Date of Patent: December 21, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Daniel Sairom Krishnan Hewlett, Dan Liu, Qi Guo, Wenxiang Chen, Xiaoyi Zhang, Lester Gilbert Cottle, III, Xuebin Yan, Yu Gong, Haitong Tian, Siyao Sun, Pei-Lun Liao
  • TWO-STAGE TRAINING WITH NON-RANDOMIZED AND RANDOMIZED DATA
    Publication number: 20200401644
    Abstract: In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.
    Type: Application
    Filed: June 21, 2019
    Publication date: December 24, 2020
    Inventors: Daniel Sairom Krishnan Hewlett, Dan Liu, Qi Guo, Wenxiang Chen, Xiaoyi Zhang, Lester Gilbert Cottle, Xuebin Yan, Yu Gong, Haitong Tian, Siyao Sun, Pei-Lun Liao