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US Patent 11080591 Processing sequences using convolutional neural networks

Patent 11080591 was granted and assigned to Deepmind Technologies Limited on August, 2021 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
Deepmind Technologies Limited
Deepmind Technologies Limited
1
Current Assignee
Deepmind Technologies Limited
Deepmind Technologies Limited
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
110805911
Patent Inventor Names
Lasse Espeholt1
Aaron Gerard Antonius van den Oord1
Karen Simonyan1
Nal Emmerich Kalchbrenner1
Oriol Vinyals1
Sander Etienne Lea Dieleman1
Date of Patent
August 3, 2021
1
Patent Application Number
201709061
Date Filed
September 6, 2017
1
Patent Citations Received
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US Patent 11386914 Generating audio using neural networks
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US Patent 11321542 Processing text sequences using neural networks
1
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US Patent 12063226 Graph-based multi-staged attack detection in the context of an attack framework
3
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US Patent 11956253 Ranking cybersecurity alerts from multiple sources using machine learning
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US Patent 11625366 System, method, and computer program for automatic parser creation
5
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US Patent 11417314 Speech synthesis method, speech synthesis device, and electronic apparatus
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US Patent 11423143 Anomaly detection based on processes executed within a network
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US Patent 11431741 Detecting unmanaged and unauthorized assets in an information technology network with a recurrent neural network that identifies anomalously-named assets
...
Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences using convolutional neural networks. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step.

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