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公开(公告)号：US20200380023A1
公开(公告)日：2020-12-03
申请号：US16998891
申请日：2020-08-20
Applicant: Google LLC
Inventor： Gregory Sean Corrado , Tomas Mikolov , Samy Bengio , Yoram Singer , Jonathon Shlens , Andrea L. Frome , Jeffrey Adgate Dean , Mohammad Norouzi
Abstract: 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.
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公开(公告)号：US10062035B1
公开(公告)日：2018-08-28
申请号：US14104004
申请日：2013-12-12
Applicant: Google LLC
Inventor： Tal Shaked , Tushar Deepak Chandra , Yoram Singer , Tze Way Eugene Ie , Joshua Redstone
CPC classification number: G06N20/00
Abstract: The present disclosure provides methods and systems for using variable length representations of machine learning statistics. A method may include storing an n-bit representation of a first statistic at a first n-bit storage cell. A first update to the first statistic may be received, and it may be determined that the first update causes a first loss of precision of the first statistic as stored in the first n-bit storage cell. Accordingly, an m-bit representation of the first statistic may be stored at a first m-bit storage cell based on the determination. The first m-bit storage cell may be associated with the first n-bit storage cell. As a result, upon receiving an instruction to use the first statistic in a calculation, a combination of the n-bit representation and the m-bit representation may be used to perform the calculation.
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公开(公告)号：US11960519B2
公开(公告)日：2024-04-16
申请号：US16998891
申请日：2020-08-20
Applicant: Google LLC
Inventor： Gregory Sean Corrado , Tomas Mikolov , Samy Bengio , Yoram Singer , Jonathon Shlens , Andrea L Frome , Jeffrey Adgate Dean , Mohammad Norouzi
Abstract: 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.
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公开(公告)号：US10438129B1
公开(公告)日：2019-10-08
申请号：US14586043
申请日：2014-12-30
Applicant: Google LLC
Inventor： Yoram Singer , Tal Shaked , Tushar Deepak Chandra , Tze Way Eugene Ie
IPC: G06N20/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning systems. One of the methods includes receiving a plurality of training examples; and training a machine learning system on each of the plurality of training examples to determine trained values for weights of a machine learning model, wherein training the machine learning system comprises: assigning an initial value for a regularization penalty for a particular weight for a particular feature; and adjusting the initial value for the regularization penalty for the particular weight for the particular feature during the training of the machine learning system.
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公开(公告)号：US10769191B2
公开(公告)日：2020-09-08
申请号：US14576907
申请日：2014-12-19
Applicant: Google LLC
Inventor： Gregory Sean Corrado , Tomas Mikolov , Samy Bengio , Yoram Singer , Jonathon Shlens , Andrea L. Frome , Jeffrey Adgate Dean , Mohammad Norouzi
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20200012905A1
公开(公告)日：2020-01-09
申请号：US16576321
申请日：2019-09-19
Applicant: Google LLC
Inventor： Samuel Bengio , Jeffrey Adgate Dean , Quoc V. Le , Jonathon Shlens , Yoram Singer
Abstract: Systems and techniques are disclosed for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.
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公开(公告)号：US10445623B2
公开(公告)日：2019-10-15
申请号：US15488041
申请日：2017-04-14
Applicant: Google LLC
Inventor： Samy Bengio , Jeffrey Adgate Dean , Quoc V. Le , Jonathon Shlens , Yoram Singer
Abstract: Systems and techniques are disclosed for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.
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公开(公告)号：US11663520B1
公开(公告)日：2023-05-30
申请号：US16551610
申请日：2019-08-26
Applicant: Google LLC
Inventor： Yoram Singer , Tal Shaked , Tushar Deepak Chandra , Tze Way Eugene Ie
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning systems. One of the methods includes receiving a plurality of training examples; and training a machine learning system on each of the plurality of training examples to determine trained values for weights of a machine learning model, wherein training the machine learning system comprises: assigning an initial value for a regularization penalty for a particular weight for a particular feature; and adjusting the initial value for the regularization penalty for the particular weight for the particular feature during the training of the machine learning system.
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公开(公告)号：US10509772B1
公开(公告)日：2019-12-17
申请号：US15393071
申请日：2016-12-28
Applicant: Google LLC
Inventor： Tushar Deepak Chandra , Tal Shaked , Yoram Singer , Tze Way Eugene le , Joshua Redstone
IPC: G06F7/00 , G06F16/176 , G06F16/23
Abstract: The present disclosure provides systems and techniques for efficient locking of datasets in a database when updates to a dataset may be delayed. A method may include accumulating a plurality of updates to a first set of one or more values associated with one or more features. The first set of one or more values may be stored within a first database column. Next, it may be determined that a first database column update aggregation rule is satisfied. A lock assigned to at least a portion of at least a first database column may be acquired. Accordingly, one or more values in the first set within the first database column may be updated based on the plurality of updates. In an implementation, the first set of one or more values may be associated with the first lock.
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公开(公告)号：US20240220527A1
公开(公告)日：2024-07-04
申请号：US18606458
申请日：2024-03-15
Applicant: Google LLC
Inventor： Gregory Sean Corrado , Tomas Mikolov , Samuel Bengio , Yoram Singer , Jonathon Shlens , Andrea L. Frome , Jeffrey Adgate Dean , Mohammad Norouzi
Abstract: 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.
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