公开(公告)号：US11790238B2

公开(公告)日：2023-10-17

申请号：US16995655

申请日：2020-08-17

Applicant: DeepMind Technologies Limited

Inventor： Daniel Pieter Wierstra ,  Chrisantha Thomas Fernando ,  Alexander Pritzel ,  Dylan Sunil Banarse ,  Charles Blundell ,  Andrei-Alexandru Rusu ,  Yori Zwols ,  David Ha

IPC: G06N3/04 ,  G06N3/086 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/086 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045

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.

公开(公告)号：US20240249146A1

公开(公告)日：2024-07-25

申请号：US18415376

申请日：2024-01-17

Applicant: DeepMind Technologies Limited

Inventor： Chrisantha Thomas Fernando ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Hanxiao Liu ,  Oriol Vinyals

IPC: G06N3/086 ,  G06F16/901 ,  G06F17/15 ,  G06N3/045

CPC classification number: G06N3/086 ,  G06F16/9024 ,  G06N3/045 ,  G06F17/15

Abstract: 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.

公开(公告)号：US20240046106A1

公开(公告)日：2024-02-08

申请号：US18487707

申请日：2023-10-16

Applicant: DeepMind Technologies Limited

Inventor： Daniel Pieter Wierstra ,  Chrisantha Thomas Fernando ,  Alexander Pritzel ,  Dylan Sunil Banarse ,  Charles Blundell ,  Andrei-Alexandru Rusu ,  Yori Zwols ,  David Ha

IPC: G06N3/086 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/086 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045

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.

公开(公告)号：US20200293899A1

公开(公告)日：2020-09-17

申请号：US16759567

申请日：2018-10-26

Applicant: DeepMind Technologies Limited

Inventor： Chrisantha Thomas Fernando ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Hanxiao Liu ,  Oriol Vinyals

IPC: G06N3/08 ,  G06N3/04 ,  G06F16/901

Abstract: 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.

公开(公告)号：US10748065B2

公开(公告)日：2020-08-18

申请号：US16526240

申请日：2019-07-30

Applicant: DeepMind Technologies Limited

Inventor： Daniel Pieter Wierstra ,  Chrisantha Thomas Fernando ,  Alexander Pritzel ,  Dylan Sunil Banarse ,  Charles Blundell ,  Andrei-Alexandru Rusu ,  Yori Zwols ,  David Ha

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US11907853B2

公开(公告)日：2024-02-20

申请号：US16759567

申请日：2018-10-26

Applicant: DeepMind Technologies Limited

Inventor： Chrisantha Thomas Fernando ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Hanxiao Liu ,  Oriol Vinyals

IPC: G06N3/086 ,  G06N3/045 ,  G06F17/15 ,  G06F16/901

CPC classification number: G06N3/086 ,  G06F16/9024 ,  G06N3/045 ,  G06F17/15

Abstract: 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.

公开(公告)号：US20200380372A1

公开(公告)日：2020-12-03

申请号：US16995655

申请日：2020-08-17

Applicant: DeepMind Technologies Limited

Inventor： Daniel Pieter Wierstra ,  Chrisantha Thomas Fernando ,  Alexander Pritzel ,  Dylan Sunil Banarse ,  Charles Blundell ,  Andrei-Alexandru Rusu ,  Yori Zwols ,  David Ha

IPC: G06N3/08 ,  G06N3/04

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.