Patents by Inventor Jeffrey Adgate Dean
Jeffrey Adgate Dean 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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Publication number: 20220351091Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a distilled machine learning model. One of the methods includes training a cumbersome machine learning model, wherein the cumbersome machine learning model is configured to receive an input and generate a respective score for each of a plurality of classes; and training a distilled machine learning model on a plurality of training inputs, wherein the distilled machine learning model is also configured to receive inputs and generate scores for the plurality of classes, comprising: processing each training input using the cumbersome machine learning model to generate a cumbersome target soft output for the training input; and training the distilled machine learning model to, for each of the training inputs, generate a soft output that matches the cumbersome target soft output for the training input.Type: ApplicationFiled: July 13, 2022Publication date: November 3, 2022Inventors: Oriol Vinyals, Jeffrey Adgate Dean, Geoffrey E. Hinton
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Patent number: 11455514Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices.Type: GrantFiled: August 28, 2019Date of Patent: September 27, 2022Assignee: Google LLCInventors: Benoit Steiner, Anna Darling Goldie, Jeffrey Adgate Dean, Hieu Hy Pham, Azalia Mirhoseini, Quoc V. Le
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Patent number: 11423337Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a distilled machine learning model. One of the methods includes training a cumbersome machine learning model, wherein the cumbersome machine learning model is configured to receive an input and generate a respective score for each of a plurality of classes; and training a distilled machine learning model on a plurality of training inputs, wherein the distilled machine learning model is also configured to receive inputs and generate scores for the plurality of classes, comprising: processing each training input using the cumbersome machine learning model to generate a cumbersome target soft output for the training input; and training the distilled machine learning model to, for each of the training inputs, generate a soft output that matches the cumbersome target soft output for the training input.Type: GrantFiled: April 7, 2020Date of Patent: August 23, 2022Assignee: Google LLCInventors: Oriol Vinyals, Jeffrey Adgate Dean, Geoffrey E. Hinton
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Publication number: 20220222219Abstract: A method of accessing data includes storing a table that includes a plurality of tablets corresponding to distinct non-overlapping table portions. Respective pluralities of tablet access objects and application objects are stored in a plurality of servers. A distinct application object and distinct tablet are associated with each tablet access object. Each application object corresponds to a distinct instantiation of an application associated with the table. The tablet access objects and associated application objects are redistributed among the servers in accordance with a first load-balancing criterion. A first request directed to a respective tablet is received from a client. In response, the tablet access object associated with the respective tablet is used to perform a data access operation on the respective tablet, and the application object associated with the respective tablet is used to perform an additional computational operation to produce a result to be returned to the client.Type: ApplicationFiled: February 14, 2022Publication date: July 14, 2022Inventors: Jeffrey Adgate Dean, Sanjay Ghemawat, Andrew Fikes, Yasushi Saito
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Patent number: 11367112Abstract: The usefulness of content (target content), such as advertisements, may be increased by determining additional content and providing such additional content in association with the content. The target content may be text, a Web page, a URL, a search query, etc. The additional content might be related suggested queries (e.g. “Try a search for ______”), news articles (or excerpts or summaries thereof), reviews (or excerpts or summaries thereof), advertisements, user group messages, etc.Type: GrantFiled: January 15, 2020Date of Patent: June 21, 2022Assignee: Google LLCInventors: Jeffrey Adgate Dean, Krishna Bharat, Paul Buchheit
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Publication number: 20220108058Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.Type: ApplicationFiled: December 17, 2021Publication date: April 7, 2022Inventors: Anna Darling Goldie, Azalia Mirhoseini, Ebrahim Songhori, Wenjie Jiang, Shen Wang, Roger David Carpenter, Young-Joon Lee, Mustafa Nazim Yazgan, Chian-min Richard Ho, Quoc V. Le, James Laudon, Jeffrey Adgate Dean, Kavya Srinivasa Setty, Omkar Pathak
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Publication number: 20220043951Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.Type: ApplicationFiled: August 23, 2021Publication date: February 10, 2022Inventors: Chian-min Richard Ho, William Hang, Mustafa Nazim Yazgan, Anna Darling Goldie, Jeffrey Adgate Dean, Azalia Mirhoseini, Emre Tuncer, Ya Wang, Anand Babu
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Publication number: 20220019896Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for modifying a computational graph to include send and receive nodes. Communication between unique devices performing operations of different subgraphs of the computational graph can be handled efficiently by inserting send and receive nodes into each subgraph. When executed, the operations that these send and receive nodes represent may enable pairs of unique devices to conduct communication with each other in a self-sufficient manner. This shifts the burden of coordinating communication away from the backend, which affords the system that processes this computational graph representation the opportunity to perform one or more other processes while devices are executing subgraphs.Type: ApplicationFiled: August 3, 2021Publication date: January 20, 2022Inventors: Vijay Vasudevan, Jeffrey Adgate Dean, Sanjay Ghemawat
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Patent number: 11216609Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.Type: GrantFiled: April 22, 2021Date of Patent: January 4, 2022Assignee: Google LLCInventors: Anna Darling Goldie, Azalia Mirhoseini, Ebrahim Songhori, Wenjie Jiang, Shen Wang, Roger David Carpenter, Young-Joon Lee, Mustafa Nazim Yazgan, Chian-Min Richard Ho, Quoc V. Le, James Laudon, Jeffrey Adgate Dean, Kavya Srinivasa Setty, Omkar Pathak
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Publication number: 20210334445Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.Type: ApplicationFiled: April 22, 2021Publication date: October 28, 2021Inventors: Anna Darling Goldie, Azalia Mirhoseini, Ebrahim Songhori, Wenjie Jiang, Shen Wang, Roger David Carpenter, Young-Joon Lee, Mustafa Nazim Yazgan, Chian-min Richard Ho, Quoc V. Le, James Laudon, Jeffrey Adgate Dean, Kavya Srinivasa Setty, Omkar Pathak
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Publication number: 20210311909Abstract: A method for deleting obsolete files from a file system is provided. The method includes receiving a request to delete a reference to a first target file of a plurality of target files stored in a file system, the first target file having a first target file name. A first reference file whose file name includes the first target file name is identified. The first reference file is deleted from the file system. The method further includes determining whether the file system includes at least one reference file, distinct from the first reference file, whose file name includes the first target file name. In accordance with a determination that the file system does not include the at least one reference file, the first target file is deleted from the file system.Type: ApplicationFiled: June 17, 2021Publication date: October 7, 2021Inventors: Yasushi Saito, Sanjay Ghemawat, Jeffrey Adgate Dean
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Patent number: 11100266Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.Type: GrantFiled: June 1, 2020Date of Patent: August 24, 2021Assignee: Google LLCInventors: Chian-min Richard Ho, William Hang, Mustafa Nazim Yazgan, Anna Darling Goldie, Jeffrey Adgate Dean, Azalia Mirhoseini, Emre Tuncer, Ya Wang, Anand Babu
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Patent number: 11087216Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for modifying a computational graph to include send and receive nodes. Communication between unique devices performing operations of different subgraphs of the computational graph can be handled efficiently by inserting send and receive nodes into each subgraph. When executed, the operations that these send and receive nodes represent may enable pairs of unique devices to conduct communication with each other in a self-sufficient manner. This shifts the burden of coordinating communication away from the backend, which affords the system that processes this computational graph representation the opportunity to perform one or more other processes while devices are executing subgraphs.Type: GrantFiled: September 9, 2020Date of Patent: August 10, 2021Assignee: Google LLCInventors: Vijay Vasudevan, Jeffrey Adgate Dean, Sanjay Ghemawat
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Patent number: 10991005Abstract: The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.Type: GrantFiled: January 28, 2019Date of Patent: April 27, 2021Assignee: Google LLCInventors: Jeffrey Adgate Dean, Georges Harik, Paul Buchheit
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Patent number: 10949881Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.Type: GrantFiled: April 4, 2017Date of Patent: March 16, 2021Assignee: Google LLCInventors: Jeffrey Adgate Dean, Georges Harik, Paul Buchheit
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Publication number: 20210019604Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.Type: ApplicationFiled: July 27, 2020Publication date: January 21, 2021Inventors: Gregory Sean Corrado, Ilya Sutskever, Jeffrey Adgate Dean
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Publication number: 20200401897Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for modifying a computational graph to include send and receive nodes. Communication between unique devices performing operations of different subgraphs of the computational graph can be handled efficiently by inserting send and receive nodes into each subgraph. When executed, the operations that these send and receive nodes represent may enable pairs of unique devices to conduct communication with each other in a self-sufficient manner. This shifts the burden of coordinating communication away from the backend, which affords the system that processes this computational graph representation the opportunity to perform one or more other processes while devices are executing subgraphs.Type: ApplicationFiled: September 9, 2020Publication date: December 24, 2020Inventors: Vijay Vasudevan, Jeffrey Adgate Dean, Sanjay Ghemawat
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Patent number: 10860925Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a request from a client to process a computational graph; obtaining data representing the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node that represents an operation that receives, as input, an output of an operation represented by the respective first node; identifying a plurality of available devices for performing the requested operation; partitioning the computational graph into a plurality of subgraphs, each subgraph comprising one or more nodes in the computational graph; and assigning, for each subgraph, the operations represented by the one or more nodes in the subgraph to a respective available device in the plurality of available devices for operation.Type: GrantFiled: October 28, 2016Date of Patent: December 8, 2020Assignee: Google LLCInventors: Paul A. Tucker, Jeffrey Adgate Dean, Sanjay Ghemawat, Yuan Yu
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Publication number: 20200380023Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.Type: ApplicationFiled: August 20, 2020Publication date: December 3, 2020Inventors: Gregory Sean Corrado, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea L. Frome, Jeffrey Adgate Dean, Mohammad Norouzi
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Publication number: 20200364389Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.Type: ApplicationFiled: June 1, 2020Publication date: November 19, 2020Inventors: Chian-min Richard Ho, William Hang, Mustafa Nazim Yazgan, Anna Darling Goldie, Jeffrey Adgate Dean, Azalia Mirhoseini, Emre Tuncer, Ya Wang, Anand Babu