Oracle International Corporation

Anatoly Yakovlev

Hesam Fathi Moghadam

Nipun Agarwal

Venkatanathan Varadarajan

Sandeep Agrawal

Ji Eun Jang

Sam Idicula

Mark Semmelmeyer

Guanghua Shu

Navaneeth P Jamadagni

Vincent Lee

David Hopkins

Jingxiao Cai

Michael Henry Soltau Dayringer

R Maxwell Flaherty

Robert David Hopkins, Ii

Sanjay Jinturkar

Yasha Pushak

Ali Moharrer

Nikan Chavoshi

Lee Fason Hartley

J Christopher Flaherty

Daniel Pivonka

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.

Unify95: meta-learning contamination thresholds from unified anomaly scores

Distributions of on-chip inductors for monolithic voltage regulation are described. On-chip voltage regulation may be provided by integrated voltage regulators (IVRs), such as a buck converter with integrated inductors. On-chip inductors may be placed to ensure optimal voltage regulation for high power density applications. With this technology, integrated circuits may have many independent voltage domains for fine-grained dynamic voltage and frequency scaling that allows for higher overall power efficiency for the system.

Distributing on chip inductors for monolithic voltage regulation

Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.

Automated machine learning pipeline for timeseries datasets utilizing point-based algorithms

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.

Using hyperparameter predictors to improve accuracy of automatic machine learning model selection

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.

Adaptive sampling for imbalance mitigation and dataset size reduction in machine learning

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.

Method for training multichannel data receiver timing

Herein are techniques for exploring hyperparameters of a machine learning model (MLM) and to train a regressor to predict a time needed to train the MLM based on a hyperparameter configuration and a dataset. In an embodiment that is deployed in production inferencing mode, for each landmark configuration, each containing values for hyperparameters of a MLM, a computer configures the MLM based on the landmark configuration and measures time spent training the MLM on a dataset. An already trained regressor predicts time needed to train the MLM based on a proposed configuration of the MLM, dataset meta-feature values, and training durations and hyperparameter values of landmark configurations of the MLM. When instead in training mode, a regressor in training ingests a training corpus of MLM performance history to learn, by reinforcement, to predict a training time for the MLM for new datasets and/or new hyperparameter configurations.

Predicting machine learning or deep learning model training time

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Hayward, CA, United States of America

Anatoly Yakovlev