Patents by Inventor Yori Zwols

Yori Zwols 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).

  • Publication number: 20240046106
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using multi-task neural networks. One of the methods includes receiving a first network input and data identifying a first machine learning task to be performed on the first network input; selecting a path through the plurality of layers in a super neural network that is specific to the first machine learning task, the path specifying, for each of the layers, a proper subset of the modular neural networks in the layer that are designated as active when performing the first machine learning task; and causing the super neural network to process the first network input using (i) for each layer, the modular neural networks in the layer that are designated as active by the selected path and (ii) the set of one or more output layers corresponding to the identified first machine learning task.
    Type: Application
    Filed: October 16, 2023
    Publication date: February 8, 2024
    Inventors: Daniel Pieter Wierstra, Chrisantha Thomas Fernando, Alexander Pritzel, Dylan Sunil Banarse, Charles Blundell, Andrei-Alexandru Rusu, Yori Zwols, David Ha
  • Patent number: 11790238
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using multi-task neural networks. One of the methods includes receiving a first network input and data identifying a first machine learning task to be performed on the first network input; selecting a path through the plurality of layers in a super neural network that is specific to the first machine learning task, the path specifying, for each of the layers, a proper subset of the modular neural networks in the layer that are designated as active when performing the first machine learning task; and causing the super neural network to process the first network input using (i) for each layer, the modular neural networks in the layer that are designated as active by the selected path and (ii) the set of one or more output layers corresponding to the identified first machine learning task.
    Type: Grant
    Filed: August 17, 2020
    Date of Patent: October 17, 2023
    Assignee: DeepMind Technologies Limited
    Inventors: Daniel Pieter Wierstra, Chrisantha Thomas Fernando, Alexander Pritzel, Dylan Sunil Banarse, Charles Blundell, Andrei-Alexandru Rusu, Yori Zwols, David Ha
  • Publication number: 20200380372
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using multi-task neural networks. One of the methods includes receiving a first network input and data identifying a first machine learning task to be performed on the first network input; selecting a path through the plurality of layers in a super neural network that is specific to the first machine learning task, the path specifying, for each of the layers, a proper subset of the modular neural networks in the layer that are designated as active when performing the first machine learning task; and causing the super neural network to process the first network input using (i) for each layer, the modular neural networks in the layer that are designated as active by the selected path and (ii) the set of one or more output layers corresponding to the identified first machine learning task.
    Type: Application
    Filed: August 17, 2020
    Publication date: December 3, 2020
    Inventors: Daniel Pieter Wierstra, Chrisantha Thomas Fernando, Alexander Pritzel, Dylan Sunil Banarse, Charles Blundell, Andrei-Alexandru Rusu, Yori Zwols, David Ha
  • Patent number: 10748065
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using multi-task neural networks. One of the methods includes receiving a first network input and data identifying a first machine learning task to be performed on the first network input; selecting a path through the plurality of layers in a super neural network that is specific to the first machine learning task, the path specifying, for each of the layers, a proper subset of the modular neural networks in the layer that are designated as active when performing the first machine learning task; and causing the super neural network to process the first network input using (i) for each layer, the modular neural networks in the layer that are designated as active by the selected path and (ii) the set of one or more output layers corresponding to the identified first machine learning task.
    Type: Grant
    Filed: July 30, 2019
    Date of Patent: August 18, 2020
    Assignee: DeepMind Technologies Limited
    Inventors: Daniel Pieter Wierstra, Chrisantha Thomas Fernando, Alexander Pritzel, Dylan Sunil Banarse, Charles Blundell, Andrei-Alexandru Rusu, Yori Zwols, David Ha
  • Publication number: 20190354868
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using multi-task neural networks. One of the methods includes receiving a first network input and data identifying a first machine learning task to be performed on the first network input; selecting a path through the plurality of layers in a super neural network that is specific to the first machine learning task, the path specifying, for each of the layers, a proper subset of the modular neural networks in the layer that are designated as active when performing the first machine learning task; and causing the super neural network to process the first network input using (i) for each layer, the modular neural networks in the layer that are designated as active by the selected path and (ii) the set of one or more output layers corresponding to the identified first machine learning task.
    Type: Application
    Filed: July 30, 2019
    Publication date: November 21, 2019
    Inventors: Daniel Pieter Wierstra, Chrisantha Thomas Fernando, Alexander Pritzel, Dylan Sunil Banarse, Charles Blundell, Andrei-Alexandru Rusu, Yori Zwols, David Ha