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    Facilitating detection of conversation threads in a messaging channel
    12.
    发明授权
    Facilitating detection of conversation threads in a messaging channel 有权

    公开(公告)号：US11263402B2

    公开(公告)日：2022-03-01

    申请号：US16404156

    申请日：2019-05-06

    Applicant: International Business Machines Corporation

    Inventor： Ming Tan ,  Dakuo Wang ,  Mo Yu ,  Chuang Gan ,  Haoyu Wang ,  Shiyu Chang

    IPC: G06F40/30 ,  G06F40/205 ,  G06F40/289

    Abstract: Techniques facilitating detection of conversation threads in unstructured channels are provided. 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 comprise an extraction component that employs a model to detect conversation messages based on a defined confidence level and assigns the conversation messages to respective conversation thread categories. The computer executable components also can comprise a model component that trains the model on conversation messages that comprise respective text data, wherein the model is trained to detect the respective text data to the defined confidence level.

    ROBUSTNESS-AWARE QUANTIZATION FOR NEURAL NETWORKS AGAINST WEIGHT PERTURBATIONS
    13.
    发明申请
    ROBUSTNESS-AWARE QUANTIZATION FOR NEURAL NETWORKS AGAINST WEIGHT PERTURBATIONS 有权

    公开(公告)号：US20210334646A1

    公开(公告)日：2021-10-28

    申请号：US16861019

    申请日：2020-04-28

    Applicant: International Business Machines Corporation

    Inventor： Sijia Liu ,  Pin-Yu Chen ,  Gaoyuan Zhang ,  Chuang Gan

    IPC: G06N3/08 ,  G06N20/00 ,  G06F17/18

    Abstract: A method of utilizing a computing device to optimize weights within a neural network to avoid adversarial attacks includes receiving, by a computing device, a neural network for optimization. The method further includes determining, by the computing device, on a region by region basis one or more robustness bounds for weights within the neural network. The robustness bounds indicating values beyond which the neural network generates an erroneous output upon performing an adversarial attack on the neural network. The computing device further averages all robustness bounds on the region by region basis. The computing device additionally optimizes weights for adversarial proofing the neural network based at least in part on the averaged robustness bounds.

    Interpretability-Aware Adversarial Attack and Defense Method for Deep Learnings
    15.
    发明申请
    Interpretability-Aware Adversarial Attack and Defense Method for Deep Learnings 有权

    公开(公告)号：US20210216859A1

    公开(公告)日：2021-07-15

    申请号：US16742346

    申请日：2020-01-14

    Applicant: International Business Machines Corporation

    Inventor： Sijia Liu ,  Gaoyuan Zhang ,  Pin-Yu Chen ,  Chuang Gan ,  Akhilan Boopathy

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Embodiments relate to a system, program product, and method to support a convolutional neural network (CNN). A class-specific discriminative image region is localized to interpret a prediction of a CNN and to apply a class activation map (CAM) function to received input data. First and second attacks are generated on the CNN with respect to the received input data. The first attack generates first perturbed data and a corresponding first CAM, and the second attack generates second perturbed data and a corresponding second CAM. An interpretability discrepancy is measured to quantify one or more differences between the first CAM and the second CAM. The measured interpretability discrepancy is applied to the CNN. The application is a response to an inconsistency between the first CAM and the second CAM and functions to strengthen the CNN against an adversarial attack.

    VIDEO RESPONSE GENERATION AND MODIFICATION
    16.
    发明申请
    VIDEO RESPONSE GENERATION AND MODIFICATION 有权

    公开(公告)号：US20210133236A1

    公开(公告)日：2021-05-06

    申请号：US16674402

    申请日：2019-11-05

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Chuang Gan ,  Abhishek Bhandwaldar ,  Yang Zhang ,  Xiaoxiao Guo

    IPC: G06F16/73 ,  G06F16/901 ,  G06F16/783 ,  G06N20/00

    Abstract: A method, system, and program product for generating and modifying a video response is provided. The method includes receiving an audio/video file. Parsed video features of the audio/video file are generated with respect to a first graph. Parsed audio features of the audio/video file are generated with respect to a second graph. The first graph is placed overlaying the second graph and at least one intersection point between the first graph and the second graph is determined. A natural language query is executed with respect to the audio/video file and a parsed query entity is generated from the natural language query. The parsed query entity is analyzed with respect to the intersection point and a node of the intersection point comprising similar features is determined with respect to the parsed query entity. A resulting natural language response with respect to the natural language query is generated.

    Generative Adversarial Network Based Audio Restoration
    17.
    发明申请
    Generative Adversarial Network Based Audio Restoration 审中-公开

    公开(公告)号：US20200293875A1

    公开(公告)日：2020-09-17

    申请号：US16299828

    申请日：2019-03-12

    Applicant: International Business Machines Corporation

    Inventor： Yang Zhang ,  Chuang Gan

    IPC: G06N3/08

    Abstract: Mechanisms are provided for implementing a generative adversarial network (GAN) based restoration system. A first neural network of a generator of the GAN based restoration system is trained to generate an artificial audio spectrogram having a target damage characteristic based on an input audio spectrogram and a target damage vector. An original audio recording spectrogram is input to the trained generator, where the original audio recording spectrogram corresponds to an original audio recording and an input target damage vector. The trained generator processes the original audio recording spectrogram to generate an artificial audio recording spectrogram having a level of damage corresponding to the input target damage vector. A spectrogram inversion module converts the artificial audio recording spectrogram to an artificial audio recording waveform output.

    Compositional Action Machine Learning Mechanisms
    20.
    发明公开
    Compositional Action Machine Learning Mechanisms 审中-公开

    公开(公告)号：US20230360364A1

    公开(公告)日：2023-11-09

    申请号：US17737535

    申请日：2022-05-05

    Applicant: International Business Machines Corporation

    Inventor： Bo Wu ,  Chuang Gan ,  Pin-Yu Chen ,  Xin Zhang

    IPC: G06V10/764 ,  G06V10/774 ,  G06V10/80

    CPC classification number: G06V10/764 ,  G06V10/7753 ,  G06V10/806

    Abstract: Mechanisms are provided for performing machine learning (ML) training of a ML action recognition computer model which involves processing an original input dataset to generate an object feature bank comprising object feature data structures for a plurality of different objects. For an input video, a verb data structure and an original object data structure are generated and a candidate object feature data structure is selected from the object feature bank for generation of pseudo composition (PC) training data. The PC training data is generated based on the selected candidate object feature data structure and comprises a combination of the verb data structure and the candidate object feature data structure. The PC training data represents a combination of an action and an object not represented in the original input dataset. ML training of the ML action recognition computer model is performed based on an unseen combination comprising the PC training data.

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