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公开(公告)号:US20180349103A1
公开(公告)日:2018-12-06
申请号:US15721712
申请日:2017-09-29
Applicant: Apple Inc.
Inventor: Alexander B. BROWN , Michael R. SIRACUSA , Gaurav KAPOOR , Elizabeth OTTENS , Christopher M. HANSON , Zachary A. NATION , Vrushali MUNDHE , Srikrishna SRIDHAR
CPC classification number: G06F8/315 , G06F8/10 , G06F8/30 , G06F8/35 , G06F8/36 , G06F8/60 , G06F8/71 , G06N99/005
Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.
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公开(公告)号:US20210109718A1
公开(公告)日:2021-04-15
申请号:US17129782
申请日:2020-12-21
Applicant: Apple Inc.
Inventor: Alexander B. BROWN , Michael R. SIRACUSA , Gaurav KAPOOR , Elizabeth OTTENS , Christopher M. HANSON , Zachary A. NATION , Vrushali MUNDHE , Srikrishna SRIDHAR
Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.
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公开(公告)号:US20200167193A1
公开(公告)日:2020-05-28
申请号:US16776338
申请日:2020-01-29
Applicant: Apple Inc.
Inventor: Francesco ROSSI , Gaurav KAPOOR , Michael R. SIRACUSA , William B. MARCH
Abstract: The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.
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公开(公告)号:US20230176907A1
公开(公告)日:2023-06-08
申请号:US18074440
申请日:2022-12-02
Applicant: Apple Inc.
Inventor: Francesco ROSSI , Gaurav KAPOOR , Michael R. SIRACUSA , William B. MARCH
CPC classification number: G06F9/50 , G06F9/5044 , G06N3/063 , G06F8/451 , G06F9/485 , G06F9/5038 , G06N3/02 , G06F2209/509
Abstract: The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.
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公开(公告)号:US20200380415A1
公开(公告)日:2020-12-03
申请号:US16875565
申请日:2020-05-15
Applicant: Apple Inc.
Inventor: Michael R. SIRACUSA , Anil Kumar KATTI , Mohammad Reza FARHADI , Aseem WADHWA , Michael Ryan BRENNAN , Andrew Joseph RACHWALSKI
Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language), wherein the machine learning model includes a model parameter of the machine learning model. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model and the object includes an interface to update the model parameter. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.
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公开(公告)号:US20210306812A1
公开(公告)日:2021-09-30
申请号:US17345737
申请日:2021-06-11
Applicant: Apple Inc.
Inventor: Daniel C. GROSS , Patrick L. COFFMAN , Richard R. DELLINGER , Christopher P. FOSS , Jason J. GAUCI , Aria D. HAGHIGHI , Cyrus D. IRANI , Bronwyn A. JONES , Gaurav KAPOOR , Stephen O. LEMAY , Colin C. MORRIS , Michael R. SIRACUSA , Lawrence Y. YANG , Brent D. RAMERTH , Jerome R. BELLEGARDA , Jannes G.A. DOLFING , Giulia P. PAGALLO , Xin WANG , Jun HATORI , Alexandre R. MOHA , Kevin D. CLARK , Karl Christian KOHLSCHUETTER , Jesper A. ANDERSEN , Hafid ARRAS , Alexandre CARLHIAN , Thomas DENIAU , Mathieu J. MARTEL , Sofiane TOUDJI
Abstract: Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display are disclosed herein. In one aspect, a method includes presenting content in a first application. At least a portion of the content is presented without requiring input from a user. The method further includes receiving a request to open a second application. In response to receiving the request, the second application is presented with an input-receiving field. Before receiving any user input at the input-receiving field, a selectable user interface object is displayed with an indication that the portion of the content was viewed in the first application, allowing the user to paste at least the portion of the content into the input-receiving field. In response to detecting a selection of the selectable user interface object, the portion of the content is pasted into the input-receiving field.
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公开(公告)号:US20210006943A1
公开(公告)日:2021-01-07
申请号:US17030300
申请日:2020-09-23
Applicant: Apple Inc.
Inventor: Daniel C. GROSS , Patrick L. COFFMAN , Richard R. DELLINGER , Christopher P. FOSS , Jason J. GAUCI , Aria D. HAGHIGHI , Cyrus D. IRANI , Bronwyn A. JONES , Gaurav KAPOOR , Stephen O. LEMAY , Colin C. MORRIS , Michael R. SIRACUSA , Lawrence Y. YANG , Brent D. RAMERTH , Jerome R. BELLEGARDA , Jannes G.A. DOLFING , Giulia P. PAGALLO , Xin WANG , Jun HATORI , Alexandre R. MOHA , Kevin D. CLARK , Karl Christian KOHLSCHUETTER , Jesper A. ANDERSEN , Hafid ARRAS , Alexandre CARLHIAN , Thomas DENIAU , Mathieu J. MARTEL , Sofiane TOUDJI
Abstract: Systems and methods for proactively identifying and surfacing relevant content are disclosed herein. An example method includes: detecting, via the touch-sensitive display, a search activation gesture from a user of the electronic device. The method also includes: in response to detecting only the search activation gesture, displaying a search interface on substantially all of the touch-sensitive display, the search interface including: (i) a search entry portion; and (ii) a predictions portion with one or more user interface objects each associated with a respective locally-installed application. Each respective locally-installed application is selected from among a plurality of locally-installed applications for inclusion in the predictions portion based on an application usage history associated with the user of the electronic device.
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公开(公告)号:US20190286424A1
公开(公告)日:2019-09-19
申请号:US16430351
申请日:2019-06-03
Applicant: Apple Inc.
Inventor: Alexander B. BROWN , Michael R. SIRACUSA , Gaurav KAPOOR , Elizabeth OTTENS , Christopher M. HANSON , Zachary A. NATION , Vrushali MUNDHE , Srikrishna SRIDHAR
Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.
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公开(公告)号:US20180349109A1
公开(公告)日:2018-12-06
申请号:US15721701
申请日:2017-09-29
Applicant: Apple Inc.
Inventor: Alexander B. BROWN , Michael R. SIRACUSA , Gaurav KAPOOR , Elizabeth A. OTTENS , Christopher M. HANSON , Zachary A. NATION , Vrushali H. MUNDHE , Srikrishna SRIDHAR
Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.
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