Patents by Inventor Koray Kavukcuoglu
Koray Kavukcuoglu 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: 20240127060Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: ApplicationFiled: October 16, 2023Publication date: April 18, 2024Inventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Publication number: 20240119262Abstract: Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each layer in the first plurality of indexed layers is configured to receive a respective layer input and process the layer input to generate a respective layer output; and one or more subsequent DNNs corresponding to one or more respective machine learning tasks, wherein each subsequent DNN comprises a respective plurality of indexed layers, and each layer in a respective plurality of indexed layers with index greater than one receives input from a preceding layer of the respective subsequent DNN, and one or more preceding layers of respective preceding DNNs, wherein a preceding layer is a layer whose index is one less than the current index.Type: ApplicationFiled: October 2, 2023Publication date: April 11, 2024Inventors: Neil Charles Rabinowitz, Guillaume Desjardins, Andrei-Alexandru Rusu, Koray Kavukcuoglu, Raia Thais Hadsell, Razvan Pascanu, James Kirkpatrick, Hubert Josef Soyer
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Patent number: 11948075Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input data items. One of the methods includes receiving an input data item; providing the input data item as input to an encoder neural network to obtain an encoder output for the input data item; and generating a discrete latent representation of the input data item from the encoder output, comprising: for each of the latent variables, determining, from a set of latent embedding vectors in the memory, a latent embedding vector that is nearest to the encoded vector for the latent variable.Type: GrantFiled: June 11, 2018Date of Patent: April 2, 2024Assignee: DeepMind Technologies LimitedInventors: Koray Kavukcuoglu, Aaron Gerard Antonius van den Oord, Oriol Vinyals
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Patent number: 11941088Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using recurrent attention. One of the methods includes determining a location in the first image; extracting a glimpse from the first image using the location; generating a glimpse representation of the extracted glimpse; processing the glimpse representation using a recurrent neural network to update a current internal state of the recurrent neural network to generate a new internal state; processing the new internal state to select a location in a next image in the image sequence after the first image; and processing the new internal state to select an action from a predetermined set of possible actions.Type: GrantFiled: May 5, 2022Date of Patent: March 26, 2024Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Koray Kavukcuoglu
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Patent number: 11907853Abstract: A computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. Each graph has an input, an output, and a plurality of nodes between the input and the output. At each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. Each level is associated with a corresponding set of operations. At a lowest level, the operations associated with each edge are selected from a set of primitive operations. The method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. The modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.Type: GrantFiled: October 26, 2018Date of Patent: February 20, 2024Assignee: DeepMind Technologies LimitedInventors: Chrisantha Thomas Fernando, Karen Simonyan, Koray Kavukcuoglu, Hanxiao Liu, Oriol Vinyals
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Patent number: 11868894Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: GrantFiled: January 4, 2023Date of Patent: January 9, 2024Assignee: DeepMind Technologies LimitedInventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Patent number: 11842281Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward.Type: GrantFiled: February 24, 2021Date of Patent: December 12, 2023Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Wojciech Czarnecki, Maxwell Elliot Jaderberg, Tom Schaul, David Silver, Koray Kavukcuoglu
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Patent number: 11783182Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.Type: GrantFiled: February 8, 2021Date of Patent: October 10, 2023Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Adrià Puigdomènech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
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Patent number: 11775804Abstract: Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each layer in the first plurality of indexed layers is configured to receive a respective layer input and process the layer input to generate a respective layer output; and one or more subsequent DNNs corresponding to one or more respective machine learning tasks, wherein each subsequent DNN comprises a respective plurality of indexed layers, and each layer in a respective plurality of indexed layers with index greater than one receives input from a preceding layer of the respective subsequent DNN, and one or more preceding layers of respective preceding DNNs, wherein a preceding layer is a layer whose index is one less than the current index.Type: GrantFiled: March 15, 2021Date of Patent: October 3, 2023Assignee: DeepMind Technologies LimitedInventors: Neil Charles Rabinowitz, Guillaume Desjardins, Andrei-Alexandru Rusu, Koray Kavukcuoglu, Raia Thais Hadsell, Razvan Pascanu, James Kirkpatrick, Hubert Josef Soyer
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Patent number: 11734572Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.Type: GrantFiled: August 17, 2020Date of Patent: August 22, 2023Assignee: DeepMind Technologies LimitedInventors: Maxwell Elliot Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu
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Patent number: 11715009Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network including a first subnetwork followed by a second subnetwork on training inputs by optimizing an objective function. In one aspect, a method includes processing a training input using the neural network to generate a training model output, including processing a subnetwork input for the training input using the first subnetwork to generate a subnetwork activation for the training input in accordance with current values of parameters of the first subnetwork, and providing the subnetwork activation as input to the second subnetwork; determining a synthetic gradient of the objective function for the first subnetwork by processing the subnetwork activation using a synthetic gradient model in accordance with current values of parameters of the synthetic gradient model; and updating the current values of the parameters of the first subnetwork using the synthetic gradient.Type: GrantFiled: May 19, 2017Date of Patent: August 1, 2023Assignee: DeepMind Technologies LimitedInventors: Oriol Vinyals, Alexander Benjamin Graves, Wojciech Czarnecki, Koray Kavukcuoglu, Simon Osindero, Maxwell Elliot Jaderberg
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Publication number: 20230153617Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: ApplicationFiled: January 4, 2023Publication date: May 18, 2023Inventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Publication number: 20230090824Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a system configured to select actions to be performed by an agent that interacts with an environment. The system comprises a manager neural network subsystem and a worker neural network subsystem. The manager subsystem is configured to, at each of the multiple time steps, generate a final goal vector for the time step. The worker subsystem is configured to, at each of multiple time steps, use the final goal vector generated by the manager subsystem to generate a respective action score for each action in a predetermined set of actions.Type: ApplicationFiled: November 30, 2022Publication date: March 23, 2023Inventors: Simon Osindero, Koray Kavukcuoglu, Alexander Vezhnevets
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Patent number: 11593646Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: GrantFiled: February 5, 2019Date of Patent: February 28, 2023Assignee: DeepMind Technologies LimitedInventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Patent number: 11537887Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a system configured to select actions to be performed by an agent that interacts with an environment. The system comprises a manager neural network subsystem and a worker neural network subsystem. The manager subsystem is configured to, at each of the multiple time steps, generate a final goal vector for the time step. The worker subsystem is configured to, at each of multiple time steps, use the final goal vector generated by the manager subsystem to generate a respective action score for each action in a predetermined set of actions.Type: GrantFiled: May 5, 2020Date of Patent: December 27, 2022Assignee: DeepMind Technologies LimitedInventors: Simon Osindero, Koray Kavukcuoglu, Alexander Vezhnevets
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Patent number: 11507827Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed training of reinforcement learning systems. One of the methods includes receiving, by a learner, current values of the parameters of the Q network from a parameter server, wherein each learner maintains a respective learner Q network replica and a respective target Q network replica; updating, by the learner, the parameters of the learner Q network replica maintained by the learner using the current values; selecting, by the learner, an experience tuple from a respective replay memory; computing, by the learner, a gradient from the experience tuple using the learner Q network replica maintained by the learner and the target Q network replica maintained by the learner; and providing, by the learner, the computed gradient to the parameter server.Type: GrantFiled: October 14, 2019Date of Patent: November 22, 2022Assignee: DeepMind Technologies LimitedInventors: Praveen Deepak Srinivasan, Rory Fearon, Cagdas Alcicek, Arun Sarath Nair, Samuel Blackwell, Vedavyas Panneershelvam, Alessandro De Maria, Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Mustafa Suleyman
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Publication number: 20220261647Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.Type: ApplicationFiled: April 29, 2022Publication date: August 18, 2022Inventors: Volodymyr Mnih, Adrià Puigdomènech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
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Patent number: 11354548Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using recurrent attention. One of the methods includes determining a location in the first image; extracting a glimpse from the first image using the location; generating a glimpse representation of the extracted glimpse; processing the glimpse representation using a recurrent neural network to update a current internal state of the recurrent neural network to generate a new internal state; processing the new internal state to select a location in a next image in the image sequence after the first image; and processing the new internal state to select an action from a predetermined set of possible actions.Type: GrantFiled: July 13, 2020Date of Patent: June 7, 2022Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Koray Kavukcuoglu
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Patent number: 11334792Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.Type: GrantFiled: May 3, 2019Date of Patent: May 17, 2022Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Adria Puigdomenech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
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Publication number: 20210374538Abstract: We describe a method of reinforcement learning for a subject system having multiple states and actions to move from one state to the next. Training data is generated by operating on the system with a succession of actions and used to train a second neural network. Target values for training the second neural network are derived from a first neural network which is generated by copying weights of the second neural network at intervals.Type: ApplicationFiled: June 25, 2021Publication date: December 2, 2021Inventors: Volodymyr Mnih, Koray Kavukcuoglu