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    Reinforcement learning for training compression policies for machine learning models
    2.
    发明授权
    Reinforcement learning for training compression policies for machine learning models 有权

    公开(公告)号：US11501173B1

    公开(公告)日：2022-11-15

    申请号：US16831595

    申请日：2020-03-26

    Applicant: Amazon Technologies, Inc.

    Inventor： Gurumurthy Swaminathan ,  Ragav Venkatesan ,  Xiong Zhou ,  Runfei Luo ,  Vineet Khare

    IPC: G06F15/00 ,  G06F3/12 ,  G06K1/00 ,  G06N5/00 ,  G06N20/00

    Abstract: A compression policy to produce compression profiles for compressing trained machine learning models may be trained using reinforcement learning. An iterative reinforcement learning may be performed response to a search request. Different prospective compression profiles may be generated for received machine learning models according to a compression policy being trained. Performance of compressed versions of the trained neural networks according to the compression profiles may be caused using data sets used to train the machine learning models. The compression policy may be updated according to reward signal determined from an application of a reward function for performance criteria to performance results of the different versions of the machine learning models. When a search criteria is satisfied, the trained compression policy may be provided.

    Domain mapping for privacy preservation
    3.
    发明授权
    Domain mapping for privacy preservation 有权

    公开(公告)号：US10567334B1

    公开(公告)日：2020-02-18

    申请号：US16021579

    申请日：2018-06-28

    Applicant: Amazon Technologies, Inc.

    Inventor： Ragav Venkatesan ,  Gurumurthy Swaminathan

    IPC: H04L29/06 ,  H04L29/12 ,  G06F9/455 ,  G06N5/04 ,  G06N20/00 ,  H04L12/26

    Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the computer-implemented method including training a machine learning model using domain mapped third party data; and performing inference using the machine learning model by: receiving scoring data, domain mapping the received scoring data using a domain mapper that was used to generate the domain mapped third party data, and applying the machine learning model to the domain mapped received scoring data to generate an output result.

    Neural network-based image processing
    5.
    发明授权
    Neural network-based image processing 有权

    公开(公告)号：US10467729B1

    公开(公告)日：2019-11-05

    申请号：US15782390

    申请日：2017-10-12

    Applicant: Amazon Technologies, Inc.

    Inventor： Pramuditha Hemanga Perera ,  Gurumurthy Swaminathan ,  Vineet Khare

    IPC: G06K9/32 ,  G06T3/40

    Abstract: A method and system for a deep learning-based approach to image processing to increase a level of optical zooming and increasing the resolution associated with a captured image. The system includes an image capture device to generate a display of a field of view (e.g., of a scene within a viewable range of a lens of the image capture device). An indication of a desired zoom level (e.g., 1.1× to 5×) is received, and, based on this selection, a portion of the field of view is cropped. In one embodiment, the cropped portion displayed by the image capture device for a user's inspection, prior to the capturing of a low resolution image. The low resolution image is provided to an artificial neural network trained to apply a resolution up-scaling model to transform the low resolution image to a high resolution image of the cropped portion.

    Decoupled machine learning training
    8.
    发明授权
    Decoupled machine learning training 有权

    公开(公告)号：US11861490B1

    公开(公告)日：2024-01-02

    申请号：US16198726

    申请日：2018-11-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Saurabh Gupta ,  Bharathan Balaji ,  Leo Parker Dirac ,  Sahika Genc ,  Vineet Khare ,  Ragav Venkatesan ,  Gurumurthy Swaminathan

    IPC: G06N3/08 ,  G06N3/04 ,  G06F18/214 ,  G06F18/21

    CPC classification number: G06N3/08 ,  G06F18/214 ,  G06F18/2178 ,  G06N3/04

    Abstract: A machine learning environment utilizing training data generated by customer environments. A reinforced learning machine learning environment receives and processes training data generated by independently hosted, or decoupled, customer environments. The reinforced learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the decoupled customer environments. The customer environments include an agent process that collects training data and forwards the training data to the machine learning clusters without exposing the customer environment. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configured the application of the reinforced learning machine learning processes.

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