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No. Publication Number Title Publication/Patent Number Publication/Patent Number Publication Date Publication Date
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Inventor Inventor Assignee Assignee IPC IPC
1 US2020012932A1
MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE
Publication/Patent Number: US2020012932A1 Publication Date: 2020-01-09 Application Number: 16/030,859 Filing Date: 2018-07-10 Inventor: Wang, Jia-ching   Wang, Chien-yao   Yang, Chih-hsuan   Assignee: National Central University   IPC: G06N3/08 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
Machine learning device, machine learning system, and machine learning method
Publication/Patent Number: US10532432B2 Publication Date: 2020-01-14 Application Number: 16/050,252 Filing Date: 2018-07-31 Inventor: Kubo, Yoshitaka   Assignee: Fanuc Corporation   IPC: G06K9/62 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 US10572773B2
On the fly deep learning in machine learning for autonomous machines
Publication/Patent Number: US10572773B2 Publication Date: 2020-02-25 Application Number: 15/659,818 Filing Date: 2017-07-26 Inventor: Yehezkel, Rohekar Raanan Yonatan   Assignee: INTEL CORPORATION   IPC: G06K9/62 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
4 EP3602316A1
LEARNING COACH FOR MACHINE LEARNING SYSTEM
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   IPC: G06F15/18
5 US10664767B2
Machine learning apparatus, laser machining system and machine learning method
Publication/Patent Number: US10664767B2 Publication Date: 2020-05-26 Application Number: 15/460,850 Filing Date: 2017-03-16 Inventor: Takigawa, Hiroshi   Ohyama, Akinori   Assignee: FANUC CORPORATION   IPC: G06N20/00 Abstract: A machine learning apparatus that learns laser machining condition data of a laser machining system includes: a state amount observation unit that observes a state amount of the laser machining system; an operation result acquisition unit that acquires a machined result of the laser machining system; a learning unit that receives an output from the state amount observation unit and an output from the operation result acquisition unit, and learns the laser machining condition data in association with the state amount and the machined result of the laser machining system; and a decision-making unit that outputs laser machining condition data by referring to the laser machining condition data learned by the learning unit. A machine learning apparatus that learns laser machining condition data of a laser machining system includes: a state amount observation unit that observes a state amount of the laser machining system; an operation result acquisition unit that acquires a machined result of the ...More ...Less
6 US2020159924A1
PROTECTING A MACHINE LEARNING MODEL
Publication/Patent Number: US2020159924A1 Publication Date: 2020-05-21 Application Number: 16/192,787 Filing Date: 2018-11-15 Inventor: Tran, Ngoc Minh   Sinn, Mathieu   Rawat, Ambrish   Nicolae, Maria-irina   Wistuba, Martin   Assignee: International Business Machines Corporation   IPC: G06F21/56 Abstract: A method for protecting a machine learning model includes: generating a first adversarial example by modifying an original input using an attack tactic, wherein the model accurately classifies the original input but does not accurately classify at least the first adversarial example; training a defender to protect the model from the first adversarial example by updating a strategy of the defender based on predictive results from classifying the first adversarial example; updating the attack tactic based on the predictive results from classifying the first adversarial example; generating a second adversarial example by modifying the original input using the updated attack tactic, wherein the trained defender does not protect the model from the second adversarial example; and training the defender to protect the model from the second adversarial example by updating the at least one strategy of the defender based on results obtained from classifying the second adversarial example. A method for protecting a machine learning model includes: generating a first adversarial example by modifying an original input using an attack tactic, wherein the model accurately classifies the original input but does not accurately classify at least the first adversarial ...More ...Less
7 US2020012962A1
Automated Machine Learning System
Publication/Patent Number: US2020012962A1 Publication Date: 2020-01-09 Application Number: 16/416,773 Filing Date: 2019-05-20 Inventor: Dent, Killian B.   Friedman, James M.   Johnson, Allan D.   Moran, Shauna J.   Cooper, Tyler P.   Knoch, Chris K.   Magnuson, Nicholas R.   Wallace, Daniel J.   Assignee: Big Squid Inc.   IPC: G06N20/00 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
8 US10546393B2
Compression in machine learning and deep learning processing
Publication/Patent Number: US10546393B2 Publication Date: 2020-01-28 Application Number: 15/859,408 Filing Date: 2017-12-30 Inventor: Ray, Joydeep   Ashbaugh, Ben   Surti, Prasoonkumar   Ramani, Pradeep   Harihara, Rama   Justin, Jerin C.   Huang, Jing   Cui, Xiaoming   Costa, Timothy B.   Gong, Ting   Ould-ahmed-vall, Elmoustapha   Balasubramanian, Kumar   Thomas, Anil   Elibol, Oguz H.   Bobba, Jayaram   Zhuang, Guozhong   Subramanian, Bhavani   Keskin, Gokce   Sakthivel, Chandrasekaran   Poornachandran, Rajesh   Assignee: INTEL CORPORATION   IPC: G06T9/00 Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem. Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline ...More ...Less
9 US10617961B2
Online learning simulator using machine learning
Publication/Patent Number: US10617961B2 Publication Date: 2020-04-14 Application Number: 15/968,780 Filing Date: 2018-05-02 Inventor: Ma, Yanjun   Ma, Christine K.   Ma, Kevin L.   Assignee: Interlake Research, LLC   IPC: A63F13/825 Abstract: Technologies for executing a virtual character development application using machine learning are described herein. In typical simulation applications, a user may be enabled to create a virtual character and navigate a virtual world. A typical simulation application may accept inputs from the user, determine actions initiated by the user based on the inputs, determine subsequent outcomes of the user initiated actions, and mold the simulation according to the outcomes. However most outcomes may be predetermined and predictable by design. In contrast, some embodiments may include a server configured to execute a virtual character development application in conjunction with one or more client devices. A user may utilize a client device to create and develop a virtual character within the application. The user may be enabled to provide inputs to the virtual character development application, and the artificial component may process the input and extract information associated with the virtual character. Technologies for executing a virtual character development application using machine learning are described herein. In typical simulation applications, a user may be enabled to create a virtual character and navigate a virtual world. A typical simulation application may accept ...More ...Less
10 US10643127B2
Machine learning apparatus for learning condition for starting laser machining, laser apparatus, and machine learning method
Publication/Patent Number: US10643127B2 Publication Date: 2020-05-05 Application Number: 15/411,232 Filing Date: 2017-01-20 Inventor: Takigawa, Hiroshi   Yoshida, Hiroyuki   Machida, Hisatada   Maeda, Michinori   Miyata, Ryusuke   Ohyama, Akinori   Assignee: FANUC CORPORATION   IPC: B23K37/00 Abstract: The machine learning apparatus includes: a state data observing unit which observes state data of the laser apparatus, including data output from a reflected light detecting unit for measuring a reflected light amount; an operation result acquiring unit which acquires a success/failure result indicating whether the machining has been started successfully by the laser beam output from a laser oscillator; a learning unit which learns light output command data by associating the light output command data with the state data of the laser apparatus and the success/failure result of the machining start; and a decision making unit which determines the light output command data by referring to the light output command data learned by the learning unit. The machine learning apparatus includes: a state data observing unit which observes state data of the laser apparatus, including data output from a reflected light detecting unit for measuring a reflected light amount; an operation result acquiring unit which acquires a ...More ...Less