Patents by Inventor Jacob Lee Menick
Jacob Lee Menick 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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Patent number: 11977983Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.Type: GrantFiled: September 14, 2020Date of Patent: May 7, 2024Assignee: DeepMind Technologies LimitedInventors: Mohammad Gheshlaghi Azar, Meire Fortunato, Bilal Piot, Olivier Claude Pietquin, Jacob Lee Menick, Volodymyr Mnih, Charles Blundell, Remi Munos
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Patent number: 11348203Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.Type: GrantFiled: July 13, 2020Date of Patent: May 31, 2022Assignee: DeepMind Technologies LimitedInventors: Nal Emmerich Kalchbrenner, Jacob Lee Menick
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Publication number: 20210256375Abstract: A computer-implemented method for training a recurrent neural network using forward propagation rather than back propagation through time. The method is particularly suited to training sparse recurrent neural networks, and may be implemented on specialized hardware.Type: ApplicationFiled: February 5, 2021Publication date: August 19, 2021Inventors: Jacob Lee Menick, Erich Konrad Elsen, Karen Simonyan
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Publication number: 20210150355Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.Type: ApplicationFiled: January 27, 2021Publication date: May 20, 2021Inventors: Marc Gendron-Bellemare, Jacob Lee Menick, Alexander Benjamin Graves, Koray Kavukcuoglu, Remi Munos
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Publication number: 20210065012Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.Type: ApplicationFiled: September 14, 2020Publication date: March 4, 2021Inventors: Mohammad Gheshlaghi Azar, Meire Fortunato, Bilal Piot, Olivier Claude Pietquin, Jacob Lee Menick, Volodymyr Mnih, Charles Blundell, Remi Munos
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Patent number: 10936949Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.Type: GrantFiled: July 10, 2019Date of Patent: March 2, 2021Assignee: DeepMind Technologies LimitedInventors: Marc Gendron-Bellemare, Jacob Lee Menick, Alexander Benjamin Graves, Koray Kavukcuoglu, Remi Munos
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Publication number: 20210004677Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network, a decoder neural network, and a prior neural network, and using the trained networks for generative modeling, data compression, and data decompression. In one aspect, a method comprises: providing a given observation as input to the encoder neural network to generate parameters of an encoding probability distribution; determining an updated code for the given observation; selecting a code that is assigned to an additional observation; providing the code assigned to the additional observation as input to the prior neural network to generate parameters of a prior probability distribution; sampling latent variables from the encoding probability distribution; providing the latent variables as input to the decoder neural network to generate parameters of an observation probability distribution; and determining gradients of a loss function.Type: ApplicationFiled: February 11, 2019Publication date: January 7, 2021Inventors: Jacob Lee Menick, Alexander Benjamin Graves
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Publication number: 20200410643Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.Type: ApplicationFiled: July 13, 2020Publication date: December 31, 2020Inventors: Nal Emmerich Kalchbrenner, Jacob Lee Menick
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Patent number: 10839293Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.Type: GrantFiled: June 12, 2019Date of Patent: November 17, 2020Assignee: DeepMind Technologies LimitedInventors: Mohammad Gheshlaghi Azar, Meire Fortunato, Bilal Piot, Olivier Claude Pietquin, Jacob Lee Menick, Volodymyr Mnih, Charles Blundell, Remi Munos
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Patent number: 10713755Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.Type: GrantFiled: September 27, 2019Date of Patent: July 14, 2020Assignee: DeepMind Technologies LimitedInventors: Nal Emmerich Kalchbrenner, Jacob Lee Menick
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Publication number: 20200104978Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.Type: ApplicationFiled: September 27, 2019Publication date: April 2, 2020Inventors: Nal Emmerich Kalchbrenner, Jacob Lee Menick
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Publication number: 20190362238Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.Type: ApplicationFiled: June 12, 2019Publication date: November 28, 2019Inventors: Olivier Pietquin, Jacob Lee Menick, Mohammad Gheshlaghi Azar, Bilal Piot, Volodymyr Mnih, Charles Blundell, Meire Fortunato, Remi Munos
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Publication number: 20190332938Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.Type: ApplicationFiled: July 10, 2019Publication date: October 31, 2019Inventors: Marc Gendron-Bellemare, Jacob Lee Menick, Alexander Benjamin Graves, Koray Kavukcuoglu, Remi Munos