公开(公告)号：US11074503B2

公开(公告)日：2021-07-27

申请号：US15697158

申请日：2017-09-06

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Keith D. Moore ,  Elmira M. Bonab ,  Junhwan Choi

IPC: G06N3/08 ,  G06F16/2455

Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.

公开(公告)号：US09785886B1

公开(公告)日：2017-10-10

申请号：US15489564

申请日：2017-04-17

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Keith D. Moore ,  Syed Mohammad Amir Husain

IPC: G06F15/18 ,  G06N3/00 ,  G06N3/12 ,  G06N3/08 ,  G06F17/30 ,  G06N3/04

CPC classification number: G06N3/086

Abstract: A method includes, based on a fitness function, selecting a subset of models from a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model and sending the trainable model to an optimization trainer. The method includes adding a trained model received from the optimization trainer as input to a second epoch of the genetic algorithm that is subsequent to the first epoch.

公开(公告)号：US10963790B2

公开(公告)日：2021-03-30

申请号：US15582496

申请日：2017-04-28

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Keith D. Moore ,  Syed Mohammad Amir Husain

IPC: G06N3/08 ,  G16H50/70 ,  G06N3/10 ,  G06N3/04

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

公开(公告)号：US10733512B1

公开(公告)日：2020-08-04

申请号：US16716850

申请日：2019-12-17

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Udaivir Yadav ,  Tyler S. McDonnell

IPC: G06N3/08 ,  G06N3/04

Abstract: A method includes, during an epoch of a genetic algorithm, determining a fitness value for each of a plurality of autoencoders. The fitness value for an autoencoder indicates reconstruction error responsive to data representing a first operational state of one or more devices. The method includes selecting, based on the fitness values, a subset of autoencoders. The method also includes performing a genetic operation with respect to at least one autoencoder to generate a trainable autoencoder. The method includes training the trainable autoencoder to reduce a loss function value to generate a trained autoencoder. The loss function value is based on reconstruction error of the trainable autoencoder responsive to data representative of a second operational state of the device(s). The method includes adding the trained autoencoder to a population to be provided as input to a subsequent epoch of the genetic algorithm.

公开(公告)号：US20200210847A1

公开(公告)日：2020-07-02

申请号：US16811632

申请日：2020-03-06

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Keith D. Moore ,  Elmira M. Bonab ,  Junhwan Choi ,  Tyler S. McDonnell

IPC: G06N3/08

Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.

公开(公告)号：US20200175378A1

公开(公告)日：2020-06-04

申请号：US16205088

申请日：2018-11-29

Applicant: SparkCognition, Inc.

Inventor： Tyler S. McDonnell ,  Sari Andoni ,  Junhwan Choi ,  Jimmie Goode ,  Yiyun Lan ,  Keith D. Moore ,  Gavin Sellers

IPC: G06N3/08 ,  G06N3/04

Abstract: A method includes receiving, by a processor, an input data set. The input data set includes a plurality of features. The method includes determining, by the processor, one or more characteristics of the input data set. The method includes, based on the one or more characteristics, adjusting, by the processor, one or more architectural parameters of an automated model generation process. The automated model generation process is configured to generate a plurality of models using a weighted randomization process. The one or more architectural parameters weight the weighted randomization process to adjust a probability of generation of models having particular architectural features. The method further includes executing, by the processor, the automated model generation process to output a mode, the model including data representative of a neural network.

公开(公告)号：US20230186053A1

公开(公告)日：2023-06-15

申请号：US17643457

申请日：2021-12-09

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Jimmie Goode

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/0454 ,  G06N3/08

Abstract: A device includes one or more processors configured to process a portion of time-series data using a trained encoder network to generate a dimensionally reduced encoding of the portion of the time-series data. The one or more processors are further configured to process the dimensionally reduced encoding using a trained decoder network to determine decoder output data. The one or more processors are also configured to set parameters of a predictive machine-learning model based on the decoder output data, wherein the predictive machine-learning model is configured to, based on the parameters, determine a predicted future value of the time-series data.

公开(公告)号：US11610131B2

公开(公告)日：2023-03-21

申请号：US16811632

申请日：2020-03-06

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Keith D. Moore ,  Elmira M. Bonab ,  Junhwan Choi ,  Tyler S. McDonnell

IPC: G06N3/08 ,  G06N3/086 ,  G06N3/084

Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.

公开(公告)号：US20200242480A1

公开(公告)日：2020-07-30

申请号：US16848007

申请日：2020-04-14

Applicant: SparkCognition, Inc.

Inventor： Tyler S. McDonnell ,  Sari Andoni ,  Junhwan Choi ,  Jimmie Goode ,  Yiyun Lan ,  Keith D. Moore ,  Gavin Sellers

IPC: G06N3/08 ,  G06N3/04

Abstract: A method includes receiving, by a processor, an input data set. The input data set includes a plurality of features. The method includes determining, by the processor, one or more characteristics of the input data set. The method includes, based on the one or more characteristics, adjusting, by the processor, one or more architectural parameters of an automated model generation process. The automated model generation process is configured to generate a plurality of models using a weighted randomization process. The one or more architectural parameters weight the weighted randomization process to adjust a probability of generation of models having particular architectural features. The method further includes executing, by the processor, the automated model generation process to output a mode, the model including data representative of a neural network.

公开(公告)号：US10373056B1

公开(公告)日：2019-08-06

申请号：US15880339

申请日：2018-01-25

Applicant: SparkCognition, Inc.

Inventor： Sari Andoni ,  Kevin Gullikson

IPC: G06N3/08 ,  G06K9/62 ,  G06F17/18 ,  G06N3/04

Abstract: During training mode, first input data is provided to a first neural network to generate first output data indicating that the first input data is classified in a first cluster. The first input data includes at least one of a continuous feature or a categorical feature. Second input data is generated and provided to at least one second neural network to generate second output data. The at least one second neural network corresponds to a variational autoencoder. An aggregate loss corresponding to the second output data is determined, including at least one of evaluating a first loss function for the continuous feature or evaluating a second loss function for the categorical feature. Based on the aggregate loss, at least one parameter of at least one neural network is adjusted. During use mode, the neural networks are used to determine cluster identifications and anomaly likelihoods for received data samples.