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    Interpretable Tabular Data Learning Using Sequential Sparse Attention
    11.
    发明申请
    Interpretable Tabular Data Learning Using Sequential Sparse Attention 有权

    公开(公告)号：US20210034977A1

    公开(公告)日：2021-02-04

    申请号：US16945898

    申请日：2020-08-02

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Tomas Jon Pfister

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method of interpreting tabular data includes receiving, at a deep tabular data learning network (TabNet) executing on data processing hardware, a set of features. For each of multiple sequential processing steps, the method also includes: selecting, using a sparse mask of the TabNet, a subset of relevant features of the set of features; processing using a feature transformer of the TabNet, the subset of relevant features to generate a decision step output and information for a next processing step in the multiple sequential processing steps; and providing the information to the next processing step. The method also includes determining a final decision output by aggregating the decision step outputs generated for the multiple sequential processing steps.

    TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS
    14.
    发明申请
    TRAINING IMAGE-TO-IMAGE TRANSLATION NEURAL NETWORKS 有权

    公开(公告)号：US20220067441A1

    公开(公告)日：2022-03-03

    申请号：US17454516

    申请日：2021-11-11

    Applicant: Google LLC

    Inventor： Rui Zhang ,  Jia Li ,  Tomas Jon Pfister

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: A method includes obtaining a source training dataset that includes a plurality of source training images and obtaining a target training dataset that includes a plurality of target training images. For each source training image, the method includes translating, using the forward generator neural network G, the source training image to a respective translated target image according to current values of forward generator parameters. For each target training image, the method includes translating, using a backward generator neural network F, the target training image to a respective translated source image according to current values of backward generator parameters. The method also includes training the forward generator neural network G jointly with the backward generator neural network F by adjusting the current values of the forward generator parameters and the backward generator parameters to optimize an objective function.

    DISTANCE-BASED LEARNING CONFIDENCE MODEL
    15.
    发明申请
    DISTANCE-BASED LEARNING CONFIDENCE MODEL 有权

    公开(公告)号：US20210279517A1

    公开(公告)日：2021-09-09

    申请号：US17031144

    申请日：2020-09-24

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Chen Xing ,  Zizhao Zhang ,  Tomas Jon Pfister

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: A method for jointly training a classification model and a confidence model. The method includes receiving a training data set including a plurality of training data subsets. From two or more training data subsets in the training data set, the method includes selecting a support set of training examples and a query set of training examples. The method includes determining, using the classification model, a centroid value for each respective class. For each training example in the query set of training examples, the method includes generating, using the classification model, a query encoding, determining a class distance measure, determining a ground-truth distance, and updating parameters of the classification model. For each training example in the query set of training examples identified as being misclassified, the method further includes generating a standard deviation value, sampling a new query, and updating parameters of the confidence model based on the new query encoding.

    ROBUST TRAINING IN THE PRESENCE OF LABEL NOISE
    16.
    发明申请
    ROBUST TRAINING IN THE PRESENCE OF LABEL NOISE 有权

    公开(公告)号：US20210089964A1

    公开(公告)日：2021-03-25

    申请号：US17026225

    申请日：2020-09-19

    Applicant: Google LLC

    Inventor： Zizhao Zhang ,  Sercan Omer Arik ,  Tomas Jon Pfister ,  Han Zhang

    IPC: G06N20/00 ,  G06N5/04

    Abstract: A method for training a model comprises obtaining a set of labeled training samples each associated with a given label. For each labeled training sample, the method includes generating a pseudo label and estimating a weight of the labeled training sample indicative of an accuracy of the given label. The method also includes determining whether the weight of the labeled training sample satisfies a weight threshold. When the weight of the labeled training sample satisfies the weight threshold, the method includes adding the labeled training sample to a set of cleanly labeled training samples. Otherwise, the method includes adding the labeled training sample to a set of mislabeled training samples. The method includes training the model with the set of cleanly labeled training samples using corresponding given labels and the set of mislabeled training samples using corresponding pseudo labels.

    ACTIVE LEARNING VIA A SAMPLE CONSISTENCY ASSESSMENT
    17.
    发明申请
    ACTIVE LEARNING VIA A SAMPLE CONSISTENCY ASSESSMENT 有权

    公开(公告)号：US20210056417A1

    公开(公告)日：2021-02-25

    申请号：US17000094

    申请日：2020-08-21

    Applicant: Google LLC

    Inventor： Zizhao Zhang ,  Tomas Jon Pfister ,  Sercan Omer Arik ,  Mingfei Gao

    IPC: G06N3/08 ,  G06N20/00 ,  G06K9/62 ,  G06F7/24

    Abstract: A method for active learning includes obtaining a set of unlabeled training samples and for each unlabeled training sample, perturbing the unlabeled training sample to generate an augmented training sample. The method includes generating, using a machine learning model, a predicted label for both samples and determining an inconsistency value for the unlabeled training sample that represents variance between the predicted labels for the unlabeled and augmented training samples. The method includes sorting the unlabeled training samples based on the inconsistency values and obtaining, for a threshold number of samples selected from the sorted unlabeled training samples, a ground truth label. The method includes selecting a current set of labeled training samples including each selected unlabeled training samples paired with the corresponding ground truth label. The method includes training, using the current set and a proper subset of unlabeled training samples, the machine learning model.

    Active learning via a sample consistency assessment
    18.
    发明授权
    Active learning via a sample consistency assessment 有权

    公开(公告)号：US12271822B2

    公开(公告)日：2025-04-08

    申请号：US17000094

    申请日：2020-08-21

    Applicant: Google LLC

    Inventor： Zizhao Zhang ,  Tomas Jon Pfister ,  Sercan Omer Arik ,  Mingfei Gao

    IPC: G06N3/044 ,  G06F7/24 ,  G06F18/211 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G06N20/00

    Abstract: A method for active learning includes obtaining a set of unlabeled training samples and for each unlabeled training sample, perturbing the unlabeled training sample to generate an augmented training sample. The method includes generating, using a machine learning model, a predicted label for both samples and determining an inconsistency value for the unlabeled training sample that represents variance between the predicted labels for the unlabeled and augmented training samples. The method includes sorting the unlabeled training samples based on the inconsistency values and obtaining, for a threshold number of samples selected from the sorted unlabeled training samples, a ground truth label. The method includes selecting a current set of labeled training samples including each selected unlabeled training samples paired with the corresponding ground truth label. The method includes training, using the current set and a proper subset of unlabeled training samples, the machine learning model.

    Distance-based learning confidence model
    19.
    发明授权
    Distance-based learning confidence model 有权

    公开(公告)号：US12039443B2

    公开(公告)日：2024-07-16

    申请号：US18045722

    申请日：2022-10-11

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Chen Xing ,  Zizhao Zhang ,  Tomas Jon Pfister

    IPC: G06N3/08 ,  G06F18/214 ,  G06F18/2413 ,  G06F18/2431 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06F18/2148 ,  G06F18/2413 ,  G06F18/2431 ,  G06N3/04

    Abstract: A method includes receiving a training data set including a plurality of training data subsets. From two or more training data subsets in the training data set, the method includes selecting a support set of training examples and a query set of training examples. The method includes determining, using the classification model, a centroid value for each respective class. For each training example in the query set of training examples, the method includes generating, using the classification model, a query encoding, determining a class distance measure, determining a ground-truth distance, and updating parameters of the classification model. For each training example in the query set of training examples identified as being misclassified, the method further includes generating a standard deviation value, sampling a new query, and updating parameters of the confidence model based on the new query encoding.

    Interpretable tabular data learning using sequential sparse attention
    20.
    发明授权
    Interpretable tabular data learning using sequential sparse attention 有权

    公开(公告)号：US12026614B2

    公开(公告)日：2024-07-02

    申请号：US16945898

    申请日：2020-08-02

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Tomas Jon Pfister

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: A method of interpreting tabular data includes receiving, at a deep tabular data learning network (TabNet) executing on data processing hardware, a set of features. For each of multiple sequential processing steps, the method also includes: selecting, using a sparse mask of the TabNet, a subset of relevant features of the set of features; processing using a feature transformer of the TabNet, the subset of relevant features to generate a decision step output and information for a next processing step in the multiple sequential processing steps; and providing the information to the next processing step. The method also includes determining a final decision output by aggregating the decision step outputs generated for the multiple sequential processing steps.

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