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    Video frame synthesis using tensor neural networks
    74.
    发明授权
    Video frame synthesis using tensor neural networks 有权

    公开(公告)号：US11553139B2

    公开(公告)日：2023-01-10

    申请号：US17036624

    申请日：2020-09-29

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Bo Wu ,  Chuang Gan ,  Tengfei Ma ,  Dakuo Wang

    IPC: H04N5/262 ,  G06N3/04

    Abstract: A method for implementing video frame synthesis using a tensor neural network includes receiving input video data including one or more missing frames, converting the input video data into an input tensor, generating, through tensor completion based on the input tensor, output video data including one or more synthesized frames corresponding to the one or more missing frames by using a transform-based tensor neural network (TTNet) including a plurality of phases implementing a tensor iterative shrinkage thresholding algorithm (ISTA), and obtaining a loss function based on the output video data.

    Summarizing videos via side information
    75.
    发明授权
    Summarizing videos via side information 有权

    公开(公告)号：US11538248B2

    公开(公告)日：2022-12-27

    申请号：US17081239

    申请日：2020-10-27

    Applicant: International Business Machines Corporation

    Inventor： Rameswar Panda ,  Chuang Gan ,  Pin-Yu Chen ,  Bo Wu

    IPC: G06V20/00 ,  G06V20/40 ,  G06F16/783 ,  G06N20/00

    Abstract: Machine learning-based techniques for summarizing collections of data such as image and video data leveraging side information obtained from related (e.g., video) data are provided. In one aspect, a method for video summarization includes: obtaining related videos having content related to a target video; and creating a summary of the target video using information provided by the target video and side information provided by the related videos to select portions of the target video to include in the summary. The side information can include video data, still image data, text, comments, natural language descriptions, and combinations thereof.

    AUTOMATED CONVERSATIONAL RESPONSE GENERATION
    76.
    发明申请
    AUTOMATED CONVERSATIONAL RESPONSE GENERATION 有权

    公开(公告)号：US20220377028A1

    公开(公告)日：2022-11-24

    申请号：US17307175

    申请日：2021-05-04

    Applicant: International Business Machines Corporation

    Inventor： Dakuo Wang ,  Mo Yu ,  Chuang Gan ,  Bo Wu

    IPC: H04L12/58 ,  G06F40/30 ,  G06K9/62 ,  G06F11/30

    Abstract: Systems, computer-implemented methods, and/or computer program products facilitating a process to identify and respond to a primary electronic message are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a determination component can determine that a primary electronic message has not received a response electronic message. An analysis component can generate a generated electronic message addressing the informational or emotional content of the primary electronic message. In one or more embodiments, an updating component can update the analytical model based on one or more feedbacks to the generated electronic message, where the analytical model can remain active while being updated. The one or more feedbacks can comprise a feedback from an entity-in-the-loop monitoring outputs of the analytical model including the generated electronic message.

    Distributed Adversarial Training for Robust Deep Neural Networks
    78.
    发明申请
    Distributed Adversarial Training for Robust Deep Neural Networks 有权

    公开(公告)号：US20220261626A1

    公开(公告)日：2022-08-18

    申请号：US17170343

    申请日：2021-02-08

    Applicant: International Business Machines Corporation

    Inventor： Sijia Liu ,  Gaoyuan ZHANG ,  Pin-Yu Chen ,  Chuang Gan ,  Songtao Lu

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Scalable distributed adversarial training techniques for robust deep neural networks are provided. In one aspect, a method for adversarial training of a deep neural network-based model by distributed computing machines M includes, by distributed computing machines M: obtaining adversarial perturbation-modified training examples for samples in a local dataset D(i); computing gradients of a local cost function fi with respect to parameters θ of the deep neural network-based model using the adversarial perturbation-modified training examples; transmitting the gradients of the local cost function fi to a server which aggregates the gradients of the local cost function fi and transmits an aggregated gradient to the distributed computing machines M; and updating the parameters θ of the deep neural network-based model stored at each of the distributed computing machines M based on the aggregated gradient received from the server. A method for distributed adversarial training of a deep neural network-based model by the server is also provided.

    Learning-based automation machine learning code annotation in computational notebooks
    79.
    发明授权
    Learning-based automation machine learning code annotation in computational notebooks 有权

    公开(公告)号：US11360763B2

    公开(公告)日：2022-06-14

    申请号：US17069402

    申请日：2020-10-13

    Applicant: International Business Machines Corporation

    Inventor： Dakuo Wang ,  Lingfei Wu ,  Yi Wang ,  Xuye Liu ,  Chuang Gan ,  Si Er Han ,  Bei Chen ,  Ji Hui Yang

    IPC: G06F8/73 ,  G06F40/169 ,  G06N20/00

    Abstract: One embodiment of the invention provides a method for automated code annotation in machine learning (ML) and data science. The method comprises receiving, as input, a section of executable code. The method further comprises classifying, via a ML model, the section of executable code with a stage classification label indicative of a stage within a workflow for automated ML that the executable code applies to. The method further comprises categorizing, based on the stage classification label, the section of executable code with a category of annotation that is most appropriate for the section of executable code. The method further comprises generating a suggested annotation for the section of executable code based on the category of annotation. The method further comprises providing, as output, the suggested annotation to a display of an electronic device for user review. The suggested annotation is user interactable via the electronic device.

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