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公开(公告)号：US20240267532A1
公开(公告)日：2024-08-08
申请号：US18565008
申请日：2022-05-30
Applicant: DeepMind Technologies Limited
Inventor： Anton Zhernov , Chenjie Gu , Daniel J. Mankowitz , Julian Schrittwieser , Amol Balkishan Mandhane , Mary Elizabeth Rauh , Miaosen Wang , Thomas Keisuke Hubert
IPC: H04N19/149 , H04N19/172
CPC classification number: H04N19/149 , H04N19/172
Abstract: Systems and methods for training rate control neural networks through reinforcement learning. During training, reward values for training examples are generated from the current performance of the rate control neural network in encoding the video in the training example and the historical performance of the rate control neural network in encoding the video in the training example.
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公开(公告)号：US20210149968A1
公开(公告)日：2021-05-20
申请号：US16951920
申请日：2020-11-18
Applicant: DeepMind Technologies Limited
Inventor： Anton Zhernov , Krishnamurthy Dvijotham , Xiaohong Gong , Amogh S. Asgekar
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for re-ranking a collection of documents according to a first metric and subject to a constraint on a function of one or more second metrics. One of the methods includes: obtaining, for each document in the first collection of documents, a respective first metric value corresponding to the first metric and respective one or more second metric values corresponding to the one or more second metrics; re-ranking the first collection of documents, comprising: determining the constraint on the function of one or more second metrics by computing a first threshold value using a variable threshold function that takes as input second metric values for the documents in the first collection of documents; and determining the re-ranking for the first collection of documents by solving a constrained optimization for the first metric constrained by the first threshold value.
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Patent Agency Ranking
公开(公告)号：US12001484B2
公开(公告)日：2024-06-04
申请号：US17177097
申请日：2021-02-16
Applicant: DeepMind Technologies Limited
Inventor： Timothy Arthur Mann , Ivan Lobov , Anton Zhernov , Krishnamurthy Dvijotham , Xiaohong Gong , Dan-Andrei Calian
IPC: G06F16/95 , G06F16/903 , G06F17/11 , G06F17/16
CPC classification number: G06F16/90335 , G06F17/11 , G06F17/16
Abstract: Methods and systems for low-latency multi-constraint ranking of content items. One of the methods includes receiving a request to rank a plurality of content items for presentation to a user to maximize a primary objective subject to a plurality of constraints; initializing a dual variable vector; updating the dual variable vector, comprising: determining an overall objective score for the dual variable vector; identifying a plurality of candidate dual variable vectors that includes one or more neighboring node dual variable vectors; determining respective overall objective scores for each of the one or more candidate dual variable vectors; identifying the candidate with the best overall objective score; and determining whether to update the dual variable vector based on whether the identified candidate has a better overall objective score than the dual variable vector; and determining a final ranking for the content items based on the dual variable vector.
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公开(公告)号：US11847414B2
公开(公告)日：2023-12-19
申请号：US17239284
申请日：2021-04-23
Applicant: DeepMind Technologies Limited
Inventor： Krishnamurthy Dvijotham , Anton Zhernov , Sven Adrian Gowal , Conrad Grobler , Robert Stanforth
IPC: G06F40/279 , G06F40/247 , G06N5/04 , G06N20/00 , G06F40/166
CPC classification number: G06F40/279 , G06F40/166 , G06F40/247 , G06N5/04 , G06N20/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text classification machine learning model. One of the methods includes training a model having a plurality of parameters and configured to generate a classification of a text sample comprising a plurality of words by processing a model input that includes a combined feature representation of the plurality of words in the text sample, wherein the training comprises receiving a text sample and a target classification for the text sample; generating a plurality of perturbed combined feature representations; determining, based on the plurality of perturbed combined feature representations, a region in the embedding space; and determining an update to the parameters based on an adversarial objective that encourages the model to assign the target classification for the text sample for all of the combined feature representations in the region in the embedding space.
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公开(公告)号：US11675855B2
公开(公告)日：2023-06-13
申请号：US16951920
申请日：2020-11-18
Applicant: DeepMind Technologies Limited
Inventor： Anton Zhernov , Krishnamurthy Dvijotham , Xiaohong Gong , Amogh S. Asgekar
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for re-ranking a collection of documents according to a first metric and subject to a constraint on a function of one or more second metrics. One of the methods includes: obtaining, for each document in the first collection of documents, a respective first metric value corresponding to the first metric and respective one or more second metric values corresponding to the one or more second metrics; re-ranking the first collection of documents, comprising: determining the constraint on the function of one or more second metrics by computing a first threshold value using a variable threshold function that takes as input second metric values for the documents in the first collection of documents; and determining the re-ranking for the first collection of documents by solving a constrained optimization for the first metric constrained by the first threshold value.
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公开(公告)号：US20210334459A1
公开(公告)日：2021-10-28
申请号：US17239284
申请日：2021-04-23
Applicant: DeepMind Technologies Limited
Inventor： Krishnamurthy Dvijotham , Anton Zhernov , Sven Adrian Gowal , Conrad Grobler , Robert Stanforth
IPC: G06F40/279 , G06F40/247 , G06F40/166 , G06N20/00 , G06N5/04
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text classification machine learning model. One of the methods includes training a model having a plurality of parameters and configured to generate a classification of a text sample comprising a plurality of words by processing a model input that includes a combined feature representation of the plurality of words in the text sample, wherein the training comprises receiving a text sample and a target classification for the text sample; generating a plurality of perturbed combined feature representations; determining, based on the plurality of perturbed combined feature representations, a region in the embedding space; and determining an update to the parameters based on an adversarial objective that encourages the model to assign the target classification for the text sample for all of the combined feature representations in the region in the embedding space.
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公开(公告)号：US20210256072A1
公开(公告)日：2021-08-19
申请号：US17177097
申请日：2021-02-16
Applicant: DeepMind Technologies Limited
Inventor： Timothy Arthur Mann , Ivan Lobov , Anton Zhernov , Krishnamurthy Dvijotham , Xiaohong Gong , Dan-Andrei Calian
IPC: G06F16/903 , G06F17/16 , G06F17/11
Abstract: Methods and systems for low-latency multi-constraint ranking of content items. One of the methods includes receiving a request to rank a plurality of content items for presentation to a user to maximize a primary objective subject to a plurality of constraints; initializing a dual variable vector; updating the dual variable vector, comprising: determining an overall objective score for the dual variable vector; identifying a plurality of candidate dual variable vectors that includes one or more neighboring node dual variable vectors; determining respective overall objective scores for each of the one or more candidate dual variable vectors; identifying the candidate with the best overall objective score; and determining whether to update the dual variable vector based on whether the identified candidate has a better overall objective score than the dual variable vector; and determining a final ranking for the content items based on the dual variable vector.
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