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No. Publication Number Title Publication/Patent Number Publication/Patent Number Publication Date Publication Date
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1 WO2020003434A1
MACHINE LEARNING METHOD, MACHINE LEARNING DEVICE, AND MACHINE LEARNING PROGRAM
Publication/Patent Number: WO2020003434A1 Publication Date: 2020-01-02 Application Number: 2018024566 Filing Date: 2018-06-28 Inventor: Takahashi, Wataru   Oshikawa, Shota   Assignee: SHIMADZU CORPORATION   IPC: G06T7/00 Abstract: In the present invention, a full-size learning image is reduced by an image reduction unit (11), the reduced image is input to a fully convolutional neural network (FCN) computation unit (13), and the FCN computation unit (13) performs computation under set filter coefficients to output a reduced label image. The reduced label image is enlarged to full size by an image enlargement unit (14), an error calculation unit (15) calculates an error between the enlarged label image and a full-size correct image on the basis of a loss function, and a parameter updating unit (16) updates the filter coefficients in accordance with the error. It is possible to create a learning model in which optimal segmentation is executed, while including errors that occur when images are enlarged, by repeating learning under the control of a learning control unit (17). Furthermore, by including image enlargement processing in the learning model, it is possible to output a full-size label image and to evaluate the accuracy of the model with high accuracy.
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.
3 EP3683736A1
MACHINE LEARNING METHOD, MACHINE LEARNING PROGRAM, AND MACHINE LEARNING APPARATUS
Publication/Patent Number: EP3683736A1 Publication Date: 2020-07-22 Application Number: 20150943.7 Filing Date: 2020-01-09 Inventor: Nishino, Takuya   Assignee: FUJITSU LIMITED   IPC: G06N3/08 Abstract: A computer-implemented machine learning method of a machine learning model includes: performing(S1) first training of the machine learning model by using pieces of training data associated with a correct label; determining(S3,S4), from the pieces of training data, a set of pieces of training data that are close to each other in a feature space based on a core tensor generated by the trained machine learning model and have a same correct label; generating(S6) extended training data based on the determined set of pieces of training data; and performing(S7) second training of the trained machine learning model by using the generated extended training data.
4 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.
5 US202012932A1
MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE
Publication/Patent Number: US202012932A1 Publication Date: 2020-01-09 Application Number: 20/181,603 Filing Date: 2018-07-10 Inventor: Wang, Jia-ching   Yang, Chih-hsuan   Wang, Chien-yao   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.
6 US2020249892A1
PRINTER, MACHINE LEARNING DEVICE, AND MACHINE LEARNING METHOD
Publication/Patent Number: US2020249892A1 Publication Date: 2020-08-06 Application Number: 16/774,535 Filing Date: 2020-01-28 Inventor: Shikagawa, Yuichi   Tatsuda, Tetsuo   Kurane, Haruhisa   Ukita, Mamoru   Katayama, Shigenori   Tsukada, Kazunari   Assignee: SEIKO EPSON CORPORATION   IPC: G06F3/12 Abstract: A printer includes: a memory configured to store a machine-learned model obtained by machine learning using teaching data associating at least one of reflectance of a print medium, transmittance of the print medium, and image data obtained by capturing an image of a surface of the print medium with a type of the print medium; and a print controller configured to determine a type of a print medium using at least one of reflectance of the print medium, transmittance of the print medium, and image data obtained by capturing an image of a surface of the print medium and the machine-learned model.
7 US2020301376A1
MACHINE LEARNING DEVICE, CONTROL SYSTEM, AND MACHINE LEARNING
Publication/Patent Number: US2020301376A1 Publication Date: 2020-09-24 Application Number: 16/807,804 Filing Date: 2020-03-03 Inventor: Tsuneki, Ryoutarou   Ikai, Satoshi   Assignee: FANUC CORPORATION   IPC: G05B13/02 Abstract: Vibration of a machine end and an error of a moving trajectory are suppressed. A machine learning device performs machine learning of optimizing first coefficients of a filter provided in a motor controller that controls a motor and second coefficients of a velocity feedforward unit of a servo control unit provided in the motor controller on the basis of an evaluation function which is a function of measurement information after acceleration and deceleration by an external measuring instrument provided outside the motor controller, a position command input to the motor controller, and a position error which is a difference between the position command value and feedback position detection value from a detector of the servo control unit.
8 US2020285998A1
MACHINE LEARNING DEVICE AND MACHINE LEARNING METHOD
Publication/Patent Number: US2020285998A1 Publication Date: 2020-09-10 Application Number: 16/808,565 Filing Date: 2020-03-04 Inventor: Okubo, Yusuke   Hasuike, Masaharu   Assignee: JTEKT CORPORATION   IPC: G06N20/00 Abstract: A machine learning device includes a sparse modeling processing unit and a selection unit. The sparse modeling processing unit acquires individual importance degrees for each of explanatory variable candidates, the individual importance degrees being acquired by using respective sparse modeling methods different from each other, each of the sparse modeling methods taking input data including a specified objective variable in a learning model used for industrial activity and the explanatory variable candidates that are candidates for an explanatory variable for explaining the specified objective variable. The selection unit calculates a comprehensive importance degree for each of the explanatory variable candidates based on the individual importance degrees of each of the explanatory variable candidates, and selects an explanatory variable of the learning model from among the explanatory variable candidates based on the comprehensive importance degree.
9 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.
10 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
11 WO2020007962A2
MACHINE LEARNING
Publication/Patent Number: WO2020007962A2 Publication Date: 2020-01-09 Application Number: 2019067948 Filing Date: 2019-07-04 Inventor: Cronin, Leroy   Assignee: The University Court of the University of Glasgow   IPC: Abstract: The present invention provides a method to generate a predictive model for a reaction set, which reaction set is the sum of the reaction outcomes for a plurality of chemical inputs. Also provided is a system for generating a predictive model for a reaction set, which system may be used in the method. The system comprises a synthesiser for conducting reactions, which synthesiser is an automated synthesiser, an analytical unit for monitoring reactions performed by the synthesiser, and a control unit suitably programmed with a machine learning algorithm, for analysing analytical data from the analytical unit, and for controlling the synthesiser.
12 GB201917292D0
Machine learning
Publication/Patent Number: GB201917292D0 Publication Date: 2020-01-08 Application Number: 201917292 Filing Date: 2019-11-27 Inventor: Assignee: Instadeep Ltd   IPC:
13 US2020311572A1
LEARNING COACH FOR MACHINE LEARNING SYSTEM
Publication/Patent Number: US2020311572A1 Publication Date: 2020-10-01 Application Number: 16/496,585 Filing Date: 2018-03-05 Inventor: Baker, James K.   Assignee: D5AI LLC   IPC: G06N5/04 Abstract: A machine learning (ML) system includes a student ML system, a learning coach ML system, and a reference system that generates training data for the student ML system. The learning coach ML system learns to make an enhancement to the student ML system or to its learning process, such as updated hyperparameter or a network structural change, based on training of the student ML system with the training data generated by the reference system. The system may also comprise a learning experimentation system that communicates with the reference system to conduct experiments on the learning of the student learning system. Also, the learning experimentation system can determine a cost function for the learning coach ML system.
14 US2020184337A1
LEARNING COACH FOR MACHINE LEARNING SYSTEM
Publication/Patent Number: US2020184337A1 Publication Date: 2020-06-11 Application Number: 16/334,204 Filing Date: 2017-09-18 Inventor: Baker, James K.   Assignee: D5AI LLC   IPC: G06N3/08 Abstract: A machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system.
15 US2020326670A1
MACHINE LEARNING DEVICE, CONTROL DEVICE AND MACHINE LEARNING METHOD
Publication/Patent Number: US2020326670A1 Publication Date: 2020-10-15 Application Number: 16/822,576 Filing Date: 2020-03-18 Inventor: Tsuneki, Ryoutarou   Ikai, Satoshi   Assignee: FANUC CORPORATION   IPC: G05B13/02 Abstract: A machine learning device that performs reinforcement learning for a servo control device and optimizes a coefficient of a filter for attenuating a specific frequency component provided in the servo control device includes a state information acquisition unit which acquires state information that includes the result of calculation of at least one of an input/output gain of the servo control device and a phase delay of input and output, the coefficient of the filter and conditions, and an action information output unit which outputs, to the filter, action information including adjustment information of the coefficient. A reward output unit determines evaluation values under the conditions based on the result of the calculation to output, as a reward, the value of a sum of the evaluation values. A value function updating unit updates an action value function based on the value of the reward, the state information and the action information.
16 US202026248A1
MACHINE LEARNING DEVICE, CONTROL DEVICE, AND MACHINE LEARNING METHOD
Publication/Patent Number: US202026248A1 Publication Date: 2020-01-23 Application Number: 20/191,643 Filing Date: 2019-06-10 Inventor: Ikai, Satoshi   Sonoda, Naoto   Tsuneki, Ryoutarou   Assignee: Fanuc Corporation   IPC: G05B13/02 Abstract: The settling time of machine learning is shortened. A machine learning device is configured to perform machine learning related to optimization of coefficients of a transfer function of an IIR filter of a feedforward calculation unit with respect to a servo control device configured to control a servo motor configured to drive an axis of a machine tool, a robot, or an industrial machine using feedforward control by a feedforward calculation unit having the IIR filter. The machine learning device represents a zero-point at which the transfer function of the IIR filter is zero and a pole at which the transfer function diverges infinitely in polar coordinates using a radius r and an angle θ, respectively, and searches for and learns, within a predetermined search range, the radius r and the angle θ to thereby perform the optimization of the coefficients of the transfer function of the IIR filter.
17 US2020265342A1
MACHINE LEARNING PROGRAM VERIFICATION APPARATUS AND MACHINE LEARNING PROGRAM VERIFICATION METHOD
Publication/Patent Number: US2020265342A1 Publication Date: 2020-08-20 Application Number: 16/784,450 Filing Date: 2020-02-07 Inventor: Sato, Naoto   Nakagawa, Yuichiroh   Kuruma, Hironobu   Noguchi, Hideto   Assignee: HITACHI, LTD.   IPC: G06N20/20 Abstract: A validity of a prediction model can be evaluated comprehensively. A machine learning program verification apparatus 100 includes a calculation device 104. The calculation device 104 obtains a decision tree logical expression by logically combining path logical expressions indicating decision tree paths indecision trees for a program created by machine learning, creates a combined logical expression by logically combining a verification property logical expression and an objective variable calculation logical expression with the decision tree logical expression, performs satisfiability determination by inputting the combined logical expression to a satisfiability determiner, and when a result of the determination indicates satisfaction, obtains, from a satisfaction solution of the satisfiability determination, a violation input value that is a value of an explanatory variable that violates a verification property and a violation output value that is a value of an objective variable.
18 US2020265340A1
SIMILARITY BASED LEARNING MACHINE AND METHODS OF SIMILARITY BASED MACHINE LEARNING
Publication/Patent Number: US2020265340A1 Publication Date: 2020-08-20 Application Number: 16/275,681 Filing Date: 2019-02-14 Inventor: Chang, Yang   Assignee: Chang, Yang   IPC: G06N20/10 Abstract: In accordance with aspects and embodiments, an improved similarity based learning machine and methods of similarity based machine learning are provided. More specifically, the learning machines and machine learning methods of the present disclosure advantageously define subjects by attributes, assign a first similarity score to each of the subjects, from the first similarity score, calculate attribute scaling factors, and use the attribute scaling factors to generate an improved similarity score. In accordance with aspects and embodiments, the improved similarity scores may be used to improve machine learning.
19 EP3649582A1
SYSTEM AND METHOD FOR AUTOMATIC BUILDING OF LEARNING MACHINES USING LEARNING MACHINES
Publication/Patent Number: EP3649582A1 Publication Date: 2020-05-13 Application Number: 18828323.8 Filing Date: 2018-06-06 Inventor: Wong, Alexander Sheung Lai   Shafiee, Mohammad Javad   Li, Francis   Assignee: Darwinai Corporation   IPC: G06N20/00
20 US202034704A1
SEQUENTIAL LEARNING OF CONSTRAINTS FOR HIERARCHICAL REINFORCEMENT LEARNING
Publication/Patent Number: US202034704A1 Publication Date: 2020-01-30 Application Number: 20/181,604 Filing Date: 2018-07-30 Inventor: Tachibana, Ryuki   Pham, Tu-hoa   De, Magistris Giovanni De   Agravante, Don Joven Ravoy   Assignee: International Business Machines Corporation   IPC: G06N3/08 Abstract: A computer-implemented method, computer program product, and computer processing system are provided for Hierarchical Reinforcement Learning (HRL) with a target task. The method includes obtaining, by a processor device, a sequence of tasks based on hierarchical relations between the tasks, the tasks constituting the target task. The method further includes learning, by a processor device, a sequence of constraints corresponding to the sequence of tasks by repeating, for each of the tasks in the sequence, reinforcement learning and supervised learning with a set of good samples and a set of bad samples and by applying an obtained constraint for a current task to a next task.