Patents by Inventor Ruslan SALAKHUTDINOV
Ruslan SALAKHUTDINOV has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11748998Abstract: A method includes obtaining a two-dimensional image, obtaining a two-dimensional image annotation that indicates presence of an object in the two-dimensional image, obtaining three-dimensional sensor information, generating a top-down representation of the three-dimensional sensor information, and obtaining a top-down annotation that indicates presence of the object in the top-down representation. The method also includes determining a bottom surface of a three-dimensional cuboid based on map information, determining a position, a length, a width, and a yaw rotation of the three-dimensional cuboid based on the top-down annotation, and determining a height of the three-dimensional cuboid based on a two-dimensional image annotation, and the position, the length, the width, and the yaw rotation of the three-dimensional cuboid.Type: GrantFiled: May 25, 2022Date of Patent: September 5, 2023Assignee: APPLE INC.Inventors: Hanlin Goh, Nitish Srivastava, Yichuan Tang, Ruslan Salakhutdinov
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Patent number: 11636348Abstract: At a centralized model trainer, one or more neural network based models are trained using an input data set. At least a first set of parameters of a model is transmitted to a model deployment destination. Using a second input data set, one or more adaptive parameters for the model are determined at the model deployment destination. Using the adaptive parameters, one or more inferences are generated at the model deployment destination.Type: GrantFiled: November 24, 2021Date of Patent: April 25, 2023Assignee: Apple Inc.Inventors: Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov
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Patent number: 11524401Abstract: A method includes determining motion imitation information for causing a system to imitate a physical task using a first machine learning model that is trained using motion information that represents a performance of the physical task, determining a predicted correction based on the motion information and a current state from the system using a second machine learning model that is trained using the motion information, determining an action to be performed by the system based on the motion imitation information and the predicted correction; and controlling motion of the system in accordance with the action.Type: GrantFiled: March 25, 2020Date of Patent: December 13, 2022Assignee: APPLE INC.Inventors: Jian Zhang, Mario J. Srouji, Ruslan Salakhutdinov
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Patent number: 11521058Abstract: A computer-implemented system and method for storing data associated with an agent in a multi-dimensional environment via a memory architecture. The memory architecture is structured so that each unique position in the environment corresponds to a unique position within the memory architecture, thereby allowing the memory architecture to store features located at a particular position in the environment in a memory location specific to that location. As the agent traverses the environment, the agent compares the features at the agent's particular position to a summary of the features stored throughout the memory architecture and writes the features that correspond to the summary to the coordinates in the memory architecture that correspond to the agent's position. The system and method allows agents to learn, using a reinforcement signal, how to behave when acting in an environment that requires storing information over long time steps.Type: GrantFiled: June 25, 2018Date of Patent: December 6, 2022Assignee: Carnegie Mellon UniversityInventors: Ruslan Salakhutdinov, Emilio Parisotto
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Publication number: 20220343138Abstract: Sensor data captured by one or more sensors may be received at an analysis system. A neural network may be used to detect an object in the sensor data. A plurality of polygons surrounding the object may be generated in one or more subsets of the sensor data. A prediction of a future position of the object may be generated based at least in part on the polygons. One or more commands may be provided to a control system based on the prediction of the future position.Type: ApplicationFiled: June 30, 2022Publication date: October 27, 2022Applicant: Apple Inc.Inventors: Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov
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Patent number: 11468285Abstract: Sensor data captured by one or more sensors may be received at an analysis system. A neural network may be used to detect an object in the sensor data. A plurality of polygons surrounding the object may be generated in one or more subsets of the sensor data. A prediction of a future position of the object may be generated based at least in part on the polygons. One or more commands may be provided to a control system based on the prediction of the future position.Type: GrantFiled: May 26, 2017Date of Patent: October 11, 2022Assignee: Apple Inc.Inventors: Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov
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Patent number: 11373411Abstract: A method includes obtaining a two-dimensional image, obtaining a two-dimensional image annotation that indicates presence of an object in the two-dimensional image, determining a location proposal based on the two-dimensional image annotation, determining a classification for the object, determining an estimated size for the object based on the classification for the object, and defining a three-dimensional cuboid for the object based on the location proposal and the estimated size.Type: GrantFiled: June 6, 2019Date of Patent: June 28, 2022Assignee: Apple Inc.Inventors: Hanlin Goh, Nitish Srivastava, Yichuan Tang, Ruslan Salakhutdinov
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Patent number: 11189052Abstract: In accordance with some embodiments, a method is performed at a device with one or more processors and non-transitory memory. The method includes obtaining location vector data characterizing an object. The method includes determining a neural pose graph associated with a respective time-period based on an initial local pose estimation as a function of respective location vector data. The method includes determining a meta pose estimation associated with the respective time-period by aggregating the neural pose graph associated with the respective time-period and one or more other neural pose graphs associated with one or more temporally adjacent time-periods.Type: GrantFiled: August 11, 2020Date of Patent: November 30, 2021Assignee: APPLE INC.Inventors: Emilio Parisotto, Jian Zhang, Ruslan Salakhutdinov, Devendra Singh Chaplot
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Publication number: 20210090302Abstract: A method includes defining a geometric capsule that is interpretable by a capsule neural network, wherein the geometric capsule includes a feature representation and a pose. The method also includes determining multiple viewpoints relative to the geometric capsule and determining a first appearance representation of the geometric capsule for each of the multiple viewpoints. The method also includes determining a transform for each of the multiple viewpoints that moves each of the multiple viewpoints to a respective transformed viewpoint and determining second appearance representations that each correspond to one of the transformed viewpoints. The method also includes combining the second appearance representations to define an agreed appearance representation. The method also includes updating the feature representation for the geometric capsule based on the agreed appearance representation.Type: ApplicationFiled: March 31, 2020Publication date: March 25, 2021Inventors: Nitish Srivastava, Ruslan Salakhutdinov, Hanlin Goh
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Patent number: 10943148Abstract: A system employs an inspection neural network (INN) to inspect data generated during an inference process of a primary neural network (PNN) to generate an indication of reliability for an output generated by the PNN. The system includes a sensor configured to capture sensor data. Sensor data captured by the sensor is provided to a data analyzer to generate an output using the PNN. An analyzer inspector is configured to capture inspection data associated with the generation of the output by the data analyzer, and use the INN to generate an indication of reliability for the PNN's output based on the inspection data. The INN is trained using a set of training data that is distinct from the training data used to train the PNN.Type: GrantFiled: November 30, 2017Date of Patent: March 9, 2021Assignee: Apple Inc.Inventors: Rui Hu, Ruslan Salakhutdinov, Nitish Srivastava, YiChuan Tang
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Publication number: 20200372675Abstract: In accordance with some embodiments, a method is performed at a device with one or more processors and non-transitory memory. The method includes obtaining location vector data characterizing an object. The method includes determining a neural pose graph associated with a respective time-period based on an initial local pose estimation as a function of respective location vector data. The method includes determining a meta pose estimation associated with the respective time-period by aggregating the neural pose graph associated with the respective time-period and one or more other neural pose graphs associated with one or more temporally adjacent time-periods.Type: ApplicationFiled: August 11, 2020Publication date: November 26, 2020Inventors: Emilio Parisotto, Jian Zhang, Ruslan Salakhutdinov, Devendra Singh Chaplot
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Patent number: 10776948Abstract: In accordance with some embodiments, a method is performed at a device with one or more processors and non-transitory memory. The method includes obtaining location vector data characterizing an object. The method includes determining a neural pose graph associated with a respective time-period based on an initial local pose estimation as a function of respective location vector data. The method includes determining a meta pose estimation associated with the respective time-period by aggregating the neural pose graph associated with the respective time-period and one or more other neural pose graphs associated with one or more temporally adjacent time-periods.Type: GrantFiled: August 27, 2018Date of Patent: September 15, 2020Assignee: Apple Inc.Inventors: Emilio Parisotto, Jian Zhang, Ruslan Salakhutdinov, Devendra Singh Chaplot
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Patent number: 10248844Abstract: A training method of training an illumination compensation model includes extracting, from a training image, an albedo image of a face area, a surface normal image of the face area, and an illumination feature, the extracting being based on an illumination compensation model; generating an illumination restoration image based on the albedo image, the surface normal image, and the illumination feature; and training the illumination compensation model based on the training image and the illumination restoration image.Type: GrantFiled: June 22, 2016Date of Patent: April 2, 2019Assignees: SAMSUNG ELECTRONICS CO., LTD., THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTOInventors: Jungbae Kim, Ruslan Salakhutdinov, Jaejoon Han, Byungin Yoo
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Publication number: 20180373982Abstract: A computer-implemented system and method for storing data associated with an agent in a multi-dimensional environment via a memory architecture. The memory architecture is structured so that each unique position in the environment corresponds to a unique position within the memory architecture, thereby allowing the memory architecture to store features located at a particular position in the environment in a memory location specific to that location. As the agent traverses the environment, the agent compares the features at the agent's particular position to a summary of the features stored throughout the memory architecture and writes the features that correspond to the summary to the coordinates in the memory architecture that correspond to the agent's position. The system and method allows agents to learn, using a reinforcement signal, how to behave when acting in an environment that requires storing information over long time steps.Type: ApplicationFiled: June 25, 2018Publication date: December 27, 2018Inventors: Ruslan Salakhutdinov, Emilio Parisotto
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Publication number: 20180157972Abstract: A system includes a neural network organized into layers corresponding to stages of inferences. The neural network includes a common portion, a first portion, and a second portion. The first portion includes a first set of layers dedicated to performing a first inference task on an input data. The second portion includes a second set of layers dedicated to performing a second inference task on the same input data. The common portion includes a third set of layers, which may include an input layer to the neural network, that are used in the performance of both the first and second inference tasks. The system may receive an input data and perform both inference tasks on the input data in a single pass. During training, a training sample with annotations for both inference tasks may be used to train the neural network in a single pass.Type: ApplicationFiled: November 30, 2017Publication date: June 7, 2018Applicant: Apple Inc.Inventors: Rui Hu, Kshitiz Garg, Hanlin Goh, Ruslan Salakhutdinov, Nitish Srivastava, YiChuan Tang
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Publication number: 20180157934Abstract: A system employs an inspection neural network (INN) to inspect data generated during an inference process of a primary neural network (PNN) to generate an indication of reliability for an output generated by the PNN. The system includes a sensor configured to capture sensor data. Sensor data captured by the sensor is provided to a data analyzer to generate an output using the PNN. An analyzer inspector is configured to capture inspection data associated with the generation of the output by the data analyzer, and use the INN to generate an indication of reliability for the PNN's output based on the inspection data. The INN is trained using a set of training data that is distinct from the training data used to train the PNN.Type: ApplicationFiled: November 30, 2017Publication date: June 7, 2018Applicant: Apple Inc.Inventors: Rui Hu, Ruslan Salakhutdinov, Nitish Srivastava, YiChuan Tang
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Publication number: 20170046563Abstract: A training method of training an illumination compensation model includes extracting, from a training image, an albedo image of a face area, a surface normal image of the face area, and an illumination feature, the extracting being based on an illumination compensation model; generating an illumination restoration image based on the albedo image, the surface normal image, and the illumination feature; and training the illumination compensation model based on the training image and the illumination restoration image.Type: ApplicationFiled: June 22, 2016Publication date: February 16, 2017Applicants: Samsung Electronics Co., Ltd., The Governing Council of the University of TorontoInventors: Jungbae KIM, Ruslan SALAKHUTDINOV, Jaejoon HAN, Byungin YOO