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    DATA VALUATION USING REINFORCEMENT LEARNING
    2.
    发明公开
    DATA VALUATION USING REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20230325675A1

    公开(公告)日：2023-10-12

    申请号：US18333301

    申请日：2023-06-12

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Jinsung Yoon ,  Tomas Pfister

    IPC: G06N3/084 ,  G06F17/16 ,  G06N3/08 ,  G06N3/047

    CPC classification number: G06N3/084 ,  G06F17/16 ,  G06N3/08 ,  G06N3/047

    Abstract: A method includes obtaining a batch of training samples. For each particular training sample in the batch of training samples, the method includes generating, using a data value estimator model and the particular training sample, a corresponding predicted value of the particular training sample when used to train a machine learning model. The method includes selecting, based on the corresponding predicted values, a subset of the batch of training samples. For each particular training sample in the subset of the batch of training samples, the method includes determining, using the machine learning model and the particular training sample, a corresponding prediction performance measurement. The method includes adjusting one or more estimator parameter values of the data value estimator model based on the corresponding prediction performance measurements.

    Self-Supervised Learning for Temporal Counterfactual Estimation
    3.
    发明申请
    Self-Supervised Learning for Temporal Counterfactual Estimation 有权

    公开(公告)号：US20250111285A1

    公开(公告)日：2025-04-03

    申请号：US18902137

    申请日：2024-09-30

    Applicant: Google LLC

    Inventor： Yan Liu ,  Chuizheng Meng ,  Yihe Dong ,  Sercan Omer Arik ,  Tomas Pfister

    IPC: G06N20/00

    Abstract: A machine-learned model includes an encoder having a feature block configured to embed input data into a plurality of features in an embedding space. The input data includes multiple components such as covariate, treatment, and output components. The encoder includes one or more encoding layers, each including a temporal attention block and a feature-wise attention block. The temporal attention block is configured to obtain the embedded input data and apply temporal causal attention along a time dimension in parallel for each feature of the plurality of features to generate temporal embeddings. The feature-wise attention block is configured to obtain the temporal embeddings and generate component representations such as a covariate representation, a treatment representation, and an output representation.

    Multimodal Learning from Structured and Unstructured Data
    4.
    发明申请
    Multimodal Learning from Structured and Unstructured Data 有权

    公开(公告)号：US20240386321A1

    公开(公告)日：2024-11-21

    申请号：US18639519

    申请日：2024-04-18

    Applicant: Google LLC

    Inventor： Sayna Ebrahimi ,  Yihe Dong ,  Tomas Pfister ,  Sercan Omer Arik

    IPC: G06N20/00

    Abstract: Aspects of the disclosure are directed to a multimodal processing system for processing both structured and un-structured data. Real-world data is not always consistent in form or content. The multimodal processing system includes model that can be trained to account for this characteristic of real-world data, by selectively masking data of different modalities during pretraining to learn outputs that are the same or comparable between the masked and un-masked inputs. The model is trained according to modality-specific masking objectives computed for each modality of data and joint modality similarity-based masking objectives for a joint representation of the data across all modalities. The system provides consistent and accurate input, even when input data may have substantial portions of data from different modalities missing. Cross-modal relationships in data are reinforced by the model as different portions of data are masked, contributing to an overall increase in model accuracy versus other approaches.

    Aggregating Nested Vision Transformers
    6.
    发明申请
    Aggregating Nested Vision Transformers 有权

    公开(公告)号：US20220375205A1

    公开(公告)日：2022-11-24

    申请号：US17664402

    申请日：2022-05-20

    Applicant: Google LLC

    Inventor： Zizhao Zhang ,  Han Zhang ,  Long Zhao ,  Tomas Pfister

    IPC: G06V10/77 ,  G06V10/764 ,  G06V10/22 ,  G06V10/44

    Abstract: A method includes receiving image data including a series of image patches of an image. The method includes generating, using a first set of transformers of a vision transformer (V-T) model, a first set of higher order feature representations based on the series of image patches and aggregating the first set of higher order feature representations into a second set of higher order feature representations that is smaller than the first set. The method includes generating, using a second set of transformers of the V-T model, a third set of higher order feature representations based on the second set of higher order feature representations and aggregating the third set of higher order feature representations into a fourth set of higher order feature representations that is smaller than the third set. The method includes generating, using the V-T model, an image classification of the image based on the fourth set.

    Data valuation using reinforcement learning
    8.
    发明授权
    Data valuation using reinforcement learning 有权

    公开(公告)号：US12106223B2

    公开(公告)日：2024-10-01

    申请号：US18333301

    申请日：2023-06-12

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Jinsung Yoon ,  Tomas Pfister

    IPC: G06N3/04 ,  G06F17/16 ,  G06N3/047 ,  G06N3/08 ,  G06N3/084 ,  G06N3/092

    CPC classification number: G06N3/084 ,  G06F17/16 ,  G06N3/047 ,  G06N3/08 ,  G06N3/092

    Abstract: A method includes obtaining a batch of training samples. For each particular training sample in the batch of training samples, the method includes generating, using a data value estimator model and the particular training sample, a corresponding predicted value of the particular training sample when used to train a machine learning model. The method includes selecting, based on the corresponding predicted values, a subset of the batch of training samples. For each particular training sample in the subset of the batch of training samples, the method includes determining, using the machine learning model and the particular training sample, a corresponding prediction performance measurement. The method includes adjusting one or more estimator parameter values of the data value estimator model based on the corresponding prediction performance measurements.

    Complementary Prompting For Rehearsal-Free Continual Learning
    10.
    发明公开
    Complementary Prompting For Rehearsal-Free Continual Learning 审中-公开

    公开(公告)号：US20230274143A1

    公开(公告)日：2023-08-31

    申请号：US18173985

    申请日：2023-02-24

    Applicant: Google LLC

    Inventor： Zizhao Zhang ,  Zifeng Wang ,  Chen-Yu Lee ,  Ruoxi Sun ,  Sayna Ebrahimi ,  Xiaoqi Ren ,  Guolong Su ,  Vincent Perot ,  Tomas Pfister ,  Han Zhang

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: A method for rehearsal-free continual learning includes obtaining a set of training samples where training sample in the set of training samples is associated with a respective task of a plurality of different tasks. The method includes obtaining a task-invariant prompt representative of learned knowledge common to each respective task of the plurality of different tasks. The method includes, for each respective task of the plurality of different tasks, obtaining a respective task-specific prompt representative of learned knowledge specific to the respective task. The method includes, during each of one or more training iterations, for each respective training sample in the set of training samples, selecting the respective task-specific prompt representative of the respective task of the respective training sample and training a model using the task-invariant prompt and the selected respective task-specific prompt.

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