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1
US2020012932A1
Publication/Patent Number: US2020012932A1
Publication Date: 2020-01-09
Application Number: 16/030,859
Filing Date: 2018-07-10
Abstract: A machine learning method and a machine learning device are provided. The machine learning method includes: receiving an input signal and performing normalization on the input signal; transmitting the normalized input signal to a convolutional layer; and adding a sparse coding layer after the convolutional layer, wherein the sparse coding layer uses dictionary atoms to reconstruct signals on a projection of the normalized input signal passing through the convolutional layer, and the sparse coding layer receives a mini-batch input to refresh the dictionary atoms. A machine learning method and a machine learning device are provided. The machine learning method includes: receiving an input signal and performing normalization on the input signal; transmitting the normalized input signal to a convolutional layer; and adding a sparse coding ...More ...Less
2
US10532432B2
Publication/Patent Number: US10532432B2
Publication Date: 2020-01-14
Application Number: 16/050,252
Filing Date: 2018-07-31
Inventor: Kubo, Yoshitaka  
Abstract: Quality judgment on a laser beam intensity distribution is performed by taking an observation condition of the laser beam into consideration. A machine learning device includes: a state observing means that acquires data indicating an intensity distribution of a laser beam and data indicating a condition for observing the laser beam, performed to generate the data indicating the intensity distribution as input data; a label acquisition means that acquires an evaluation value related to judgment of the quality of the laser beam as a label; and a learning means that performs supervised learning using a pair of the input data acquired by the state observing means and the label acquired by the label acquisition means as training data to construct a learning model for judging the quality of the laser beam. Quality judgment on a laser beam intensity distribution is performed by taking an observation condition of the laser beam into consideration. A machine learning device includes: a state observing means that acquires data indicating an intensity distribution of a laser beam and ...More ...Less
3
US2020005180A1
Publication/Patent Number: US2020005180A1
Publication Date: 2020-01-02
Application Number: 16/267,018
Filing Date: 2019-02-04
Assignee: KenSci Inc.
Abstract: Embodiments are directed towards classifying data. A machine learning (ML) engine may select an ML model that may employ a cryptographic multi-party computation (MPC) protocol based on model preferences, including a parameter model, provided by a client. A randomness engine may be employed to provide random values and other random values based on the MPC protocol such that the random values may be provided to the client and the other random values may be provided to an answer engine. Input values that correspond to fields in the parameter model may be provided by the client such that the input values may be based on the MPC protocol and the random values. The answer engine may be employed to provide partial results to the question based on the ML model, the input values, and the MPC protocol that may be provided to the client. Embodiments are directed towards classifying data. A machine learning (ML) engine may select an ML model that may employ a cryptographic multi-party computation (MPC) protocol based on model preferences, including a parameter model, provided by a client. A randomness engine may ...More ...Less
4
US2020005187A1
Publication/Patent Number: US2020005187A1
Publication Date: 2020-01-02
Application Number: 16/506,827
Filing Date: 2019-07-09
Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable. A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that ...More ...Less
5
US2020012962A1
Publication/Patent Number: US2020012962A1
Publication Date: 2020-01-09
Application Number: 16/416,773
Filing Date: 2019-05-20
Abstract: A technology is described for automating deployment of a machine learning model. An example method may include receiving, via a graphical user interface, credentials for connecting to a data store containing a plurality of datasets and connecting to the data store using the credentials. A selection of a target metric to predict using the machine learning model can be received, via the graphical user interface, and datasets included in the plurality of datasets that correlate to the target metric can be identified by analyzing the datasets to identify an association between the target metric and data contained within the datasets. The datasets can be input to the machine learning model to train the machine learning model to generate predictions of the target metric, and the machine learning model can be deployed to computing resources in a service provider environment to generate predictions associated with the target metric. A technology is described for automating deployment of a machine learning model. An example method may include receiving, via a graphical user interface, credentials for connecting to a data store containing a plurality of datasets and connecting to the data store using the ...More ...Less
6
US2020005060A1
Publication/Patent Number: US2020005060A1
Publication Date: 2020-01-02
Application Number: 16/481,822
Filing Date: 2018-01-31
Abstract: A method for machine learning based driver assistance is provided. The method may include detecting, in one or more images of a driver operating an automobile, one or more facial landmarks. The detection of the one or more facial landmarks may include applying, to the one or more images, a first machine learning model. A gaze dynamics of the driver may be determined based at least on the one or more facial landmarks. The gaze dynamics of the driver may include a change in a gaze zone of the driver from a first gaze zone to a second gaze zone. A state of the driver may be determined based at least on the gaze dynamics of the driver. An operation of the automobile may be controlled based at least on the state of the driver. Related systems and articles of manufacture, including computer program products, are also provided. A method for machine learning based driver assistance is provided. The method may include detecting, in one or more images of a driver operating an automobile, one or more facial landmarks. The detection of the one or more facial landmarks may include applying, to the one or ...More ...Less
7
US2020013484A1
Publication/Patent Number: US2020013484A1
Publication Date: 2020-01-09
Application Number: 16/460,588
Filing Date: 2019-07-02
Assignee: GRAIL, INC.
Abstract: Systems and methods for determining a source of a variant include receiving a plurality of variants obtained from a biological sample, the variants being of unknown source upon receipt, and receiving, for each of the variants, a plurality of values for a plurality of covariates from the biological sample. The variants are input into a source assignment classifier to determine a source for each of the variants, the source being one of a plurality of possible sources. The source assignment classifier includes a plurality of coefficients associated with the plurality of covariates and a function that receives as input the values associated with each variant and the coefficients and outputs the determined source of each of the variants. Systems and methods for determining a source of a variant include receiving a plurality of variants obtained from a biological sample, the variants being of unknown source upon receipt, and receiving, for each of the variants, a plurality of values for a plurality of covariates ...More ...Less
8
US2020005194A1
Publication/Patent Number: US2020005194A1
Publication Date: 2020-01-02
Application Number: 16/024,826
Filing Date: 2018-06-30
Abstract: Techniques are provided for using machine learning techniques to associate skills with different content. In one technique, multiple classifications models are trained. Each classification model corresponds to a different skill and is trained based on textual embeddings of a plurality of content items and labels indicating whether each content item is associated with the skill that corresponds to that classification model. A particular content item embedding is generated based on text from a particular content item. The particular content item embedding is applied to the classification models to generate multiple results. One or more results of the results are identified that indicate that one or more corresponding skills are associated with the particular content item. For each result of the one or more results, skill tagging data is stored that associates the particular content item with a particular skill that corresponds to that result. Techniques are provided for using machine learning techniques to associate skills with different content. In one technique, multiple classifications models are trained. Each classification model corresponds to a different skill and is trained based on textual embeddings of a ...More ...Less
9
US10572773B2
Publication/Patent Number: US10572773B2
Publication Date: 2020-02-25
Application Number: 15/659,818
Filing Date: 2017-07-26
Abstract: A mechanism is described for facilitating on-the-fly deep learning in machine learning for autonomous machines. A method of embodiments, as described herein, includes detecting an output associated with a first deep network serving as a user-independent model associated with learning of one or more neural networks at a computing device having a processor coupled to memory. The method may further include automatically generating training data for a second deep network serving as a user-dependent model, where the training data is generated based on the output. The method may further include merging the user-independent model with the user-dependent model into a single joint model. A mechanism is described for facilitating on-the-fly deep learning in machine learning for autonomous machines. A method of embodiments, as described herein, includes detecting an output associated with a first deep network serving as a user-independent model associated with ...More ...Less
10
EP3602316A1
Publication/Patent Number: EP3602316A1
Publication Date: 2020-02-05
Application Number: 18772282.2
Filing Date: 2018-03-05
Inventor: Baker, James K.  
Assignee: D5A1 LLC
11
US2020005045A1
Publication/Patent Number: US2020005045A1
Publication Date: 2020-01-02
Application Number: 16/024,814
Filing Date: 2018-06-30
Abstract: Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model. Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature ...More ...Less
12
US2020005094A1
Publication/Patent Number: US2020005094A1
Publication Date: 2020-01-02
Application Number: 16/021,867
Filing Date: 2018-06-28
Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods. Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and ...More ...Less