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    Framework for Learning to Transfer Learn
    11.
    发明申请
    Framework for Learning to Transfer Learn 有权

    公开(公告)号：US20210034976A1

    公开(公告)日：2021-02-04

    申请号：US16945880

    申请日：2020-08-02

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Tomas Jon Pfister ,  Linchao Zhu

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method includes receiving a source data set and a target data set and identifying a loss function for a deep learning model based on the source data set and the target data set. The loss function includes encoder weights, source classifier layer weights, target classifier layer weights, coefficients, and a policy weight. During a first phase of each of a plurality of learning iterations for a learning to transfer learn (L2TL) architecture, the method also includes: applying gradient decent-based optimization to learn the encoder weights, the source classifier layer weights, and the target classifier weights that minimize the loss function; and determining the coefficients by sampling actions of a policy model. During a second phase of each of the plurality of learning iterations, determining the policy weight that maximizes an evaluation metric.

    Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
    14.
    发明公开
    Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts 审中-公开

    公开(公告)号：US20240119265A1

    公开(公告)日：2024-04-11

    申请号：US18373417

    申请日：2023-09-27

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Yihe Dong ,  Qi Yu ,  Rui Wang

    IPC: G06N3/0455 ,  G06N3/08

    CPC classification number: G06N3/0455 ,  G06N3/08

    Abstract: Aspects of the disclosure provide a deep sequence model, referred to as Koopman Neural Forecaster (KNF), for time series forecasting. KNF leverages deep neural networks (DNNs) to learn the linear Koopman space and the coefficients of chosen measurement functions. KNF imposes appropriate inductive biases for improved robustness against distributional shifts, employing both a global operator to learn shared characteristics, and a local operator to capture changing dynamics, as well as a specially-designed feedback loop to continuously update the learnt operators over time for rapidly varying behaviors. KNF achieves superior performance on multiple time series datasets that are shown to suffer from distribution shifts.

    Self-Adapting Forecasting For Multi-Horizon Forecasting Machine Learning Models
    15.
    发明申请
    Self-Adapting Forecasting For Multi-Horizon Forecasting Machine Learning Models 有权

    公开(公告)号：US20230110117A1

    公开(公告)日：2023-04-13

    申请号：US17954978

    申请日：2022-09-28

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Nathanael Christian Yoder ,  Tomas Pfister

    IPC: G06N3/04 ,  G06N3/08

    Abstract: Aspects of the disclosure provide for self-adapting forecasting (SAF) during the training and execution of machine learning models trained for multi-horizon forecasting on time-series data. The distribution of time-series data can shift over different periods of time. A deep neural network and other types of machine learning models are trained assuming that training data is independent and identically distributed (i.i.d.). With a computer system configured to execute SAF, the system can, at inference time, update a trained encoder to generate an encoded representation of time-series data capturing features characterizing the current distribution of the input time-series data. The updated encoded representation can be fed into a decoder trained to generate a multi-horizon forecast based on the updated encoded representation of the time-series data. At each instance of inference, the base weights of a trained model can be reused and updated to generate an updated encoded representation for that instance.

    Processing Multi-Horizon Forecasts For Time Series Data
    16.
    发明申请
    Processing Multi-Horizon Forecasts For Time Series Data 有权

    公开(公告)号：US20230018125A1

    公开(公告)日：2023-01-19

    申请号：US17782865

    申请日：2020-11-25

    Applicant: Google LLC

    Inventor： Si Jie Bryan Lim ,  Sercan Omer Arik ,  Nicolas Loeff ,  Tomas Pfister

    IPC: G06N3/08

    Abstract: Methods, systems, and apparatus, including computer storage media, for performing multi-horizon forecasting on time-series data. A method includes determining short-term temporal characteristics for respective forecasting horizons of one or more time-steps. The determining can include generating, using RNN encoders, encoder vectors based on static covariates, and time-varying input data; and predicting using one or more RNN decoders, a short-term pattern for a respective future time period. The method can also include capturing long-term temporal characteristics for the respective forecasting horizons based on the static covariates, the time-varying input data captured during the respective past time-periods, and the time-varying known future input data.

    Deep Neural Network Learning With Controllable Rules
    17.
    发明申请
    Deep Neural Network Learning With Controllable Rules 有权

    公开(公告)号：US20220245451A1

    公开(公告)日：2022-08-04

    申请号：US17591845

    申请日：2022-02-03

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Sungyong Seo ,  Minho Jin ,  Jinsung Yoon ,  Tomas Pfister

    IPC: G06N3/08 ,  G06N3/04

    Abstract: The present disclosure provides a method to integrate prior knowledge (referred to as rules) into deep learning in a way that can be controllable at inference without retraining or tuning the model. Deep Neural Networks with Controllable Rule Representations (DNN-CRR) incorporate a rule encoder into the model architecture, which is coupled with a corresponding rule-based objective for enabling a shared representation to be used in decision making by learning both the original task and the rule. DNN-CRR is agnostic to data type and encoder architecture and can be applied to any kind of rule defined for inputs and/or outputs. In real-world domains where incorporating rules is critical, such as prediction tasks in Physics, Retail, and Healthcare.

    DISTANCE-BASED LEARNING CONFIDENCE MODEL
    18.
    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
    19.
    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
    20.
    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.

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