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    METHOD FOR TRAINING MULTICHANNEL DATA RECEIVER TIMING
    11.
    发明申请
    METHOD FOR TRAINING MULTICHANNEL DATA RECEIVER TIMING 审中-公开

    公开(公告)号：US20200233832A1

    公开(公告)日：2020-07-23

    申请号：US16251066

    申请日：2019-01-17

    Applicant: Oracle International Corporation

    Inventor： Navaneeth P. Jamadagni ,  Ji Eun Jang ,  Anatoly Yakovlev ,  Vincent Lee ,  Guanghua Shu ,  Mark Semmelmeyer

    IPC: G06F13/42

    Abstract: An apparatus includes a first device having a clock signal and configured to communicate, via a data bus, with a second device configured to assert a data strobe signal and a plurality of data bit signals on the data bus. The first device may include a control circuit configured, during a training phase, to determine relative timing between the clock signal, the plurality of data bit signals, and the data strobe signal. The first device may determine, using a first set of sampling operations, a first timing relationship of the plurality of data bit signals relative to the data strobe signal, and determine, using a second set of sampling operations, a second timing relationship of the plurality of data bit signals and the data strobe signal relative to the clock signal. During an operational phase, the control circuit may be configured to use delays based on the first and second timing relationships to sample data from the second device on the data bus.

    UNIFY95: META-LEARNING CONTAMINATION THRESHOLDS FROM UNIFIED ANOMALY SCORES
    12.
    发明公开
    UNIFY95: META-LEARNING CONTAMINATION THRESHOLDS FROM UNIFIED ANOMALY SCORES 审中-公开

    公开(公告)号：US20240095580A1

    公开(公告)日：2024-03-21

    申请号：US17994530

    申请日：2022-11-28

    Applicant: Oracle International Corporation

    Inventor： Yasha Pushak ,  Hesam Fathi Moghadam ,  Anatoly Yakovlev ,  Robert David Hopkins, II

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Herein is a universal anomaly threshold based on several labeled datasets and transformation of anomaly scores from one or more anomaly detectors. In an embodiment, a computer meta-learns from each anomaly detection algorithm and each labeled dataset as follows. A respective anomaly detector based on the anomaly detection algorithm is trained based on the dataset. The anomaly detector infers respective anomaly scores for tuples in the dataset. The following are ensured in the anomaly scores from the anomaly detector: i) regularity that an anomaly score of zero cannot indicate an anomaly and ii) normality that an inclusive range of zero to one contains the anomaly scores from the anomaly detector. A respective anomaly threshold is calculated for the anomaly scores from the anomaly detector. After all meta-learning, a universal anomaly threshold is calculated as an average of the anomaly thresholds. An anomaly is detected based on the universal anomaly threshold.

    ONE-PASS APPROACH TO AUTOMATED TIMESERIES FORECASTING
    18.
    发明公开
    ONE-PASS APPROACH TO AUTOMATED TIMESERIES FORECASTING 审中-公开

    公开(公告)号：US20230153394A1

    公开(公告)日：2023-05-18

    申请号：US17528305

    申请日：2021-11-17

    Applicant: Oracle International Corporation

    Inventor： Ritesh Ahuja ,  Anatoly Yakovlev ,  Venkatanathan Varadarajan ,  Sandeep R. Agrawal ,  Hesam Fathi Moghadam ,  Sanjay Jinturkar ,  Nipun Agarwal

    IPC: G06K9/62 ,  G06N20/00

    CPC classification number: G06K9/6227 ,  G06K9/6257 ,  G06K9/6265 ,  G06K9/6298 ,  G06N20/00

    Abstract: Herein are timeseries preprocessing, model selection, and hyperparameter tuning techniques for forecasting development based on temporal statistics of a timeseries and a single feed-forward pass through a machine learning (ML) pipeline. In an embodiment, a computer hosts and operates the ML pipeline that automatically measures temporal statistic(s) of a timeseries. ML algorithm selection, cross validation, and hyperparameters tuning is based on the temporal statistics of the timeseries. The result from the ML pipeline is a rigorously trained and production ready ML model that is validated to have increased accuracy for multiple prediction horizons. Based on the temporal statistics, efficiency is achieved by asymmetry of investment of computer resources in the tuning and training of the most promising ML algorithm(s). Compared to other approaches, this ML pipeline produces a more accurate ML model for a given amount of computer resources and consumes fewer computer resources to achieve a given accuracy.

    Using hyperparameter predictors to improve accuracy of automatic machine learning model selection
    19.
    发明授权
    Using hyperparameter predictors to improve accuracy of automatic machine learning model selection 有权

    公开(公告)号：US11620568B2

    公开(公告)日：2023-04-04

    申请号：US16388830

    申请日：2019-04-18

    Applicant: Oracle International Corporation

    Inventor： Hesam Fathi Moghadam ,  Sandeep Agrawal ,  Venkatanathan Varadarajan ,  Anatoly Yakovlev ,  Sam Idicula ,  Nipun Agarwal

    IPC: G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  G06N3/08

    Abstract: Techniques are provided for selection of machine learning algorithms based on performance predictions by using hyperparameter predictors. In an embodiment, for each mini-machine learning model (MML model), a respective hyperparameter predictor set that predicts a respective set of hyperparameter settings for a data set is trained. Each MML model represents a respective reference machine learning model (RML model). Data set samples are generated from the data set. Meta-feature sets are generated, each meta-feature set describing a respective data set sample. A respective target set of hyperparameter settings are generated for said each MML model using a hypertuning algorithm. The meta-feature sets and the respective target set of hyperparameter settings are used to train the respective hyperparameter predictor set. Each hyperparameter predictor set is used during training and inference to improve the accuracy of automatically selecting a RML model per data set.

    Adaptive sampling for imbalance mitigation and dataset size reduction in machine learning
    20.
    发明授权
    Adaptive sampling for imbalance mitigation and dataset size reduction in machine learning 有权

    公开(公告)号：US11562178B2

    公开(公告)日：2023-01-24

    申请号：US16718164

    申请日：2019-12-17

    Applicant: Oracle International Corporation

    Inventor： Jingxiao Cai ,  Sandeep Agrawal ,  Sam Idicula ,  Venkatanathan Varadarajan ,  Anatoly Yakovlev ,  Nipun Agarwal

    IPC: G06K9/62 ,  G06N20/00

    Abstract: According to an embodiment, a method includes generating a first dataset sample from a dataset, calculating a first validation score for the first dataset sample and a machine learning model, and determining whether a difference in validation score between the first validation score and a second validation score satisfies a first criteria. If the difference in validation score does not satisfy the first criteria, the method includes generating a second dataset sample from the dataset. If the difference in validation score does satisfy the first criteria, the method includes updating a convergence value and determining whether the updated convergence value satisfies a second criteria. If the updated convergence value satisfies the second criteria, the method includes returning the first dataset sample. If the updated convergence value does not satisfy the second criteria, the method includes generating the second dataset sample from the dataset.

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