David M Ha

US Patent:

20200380372, Dec 3, 2020

Filed:

Aug 17, 2020

Appl. No.:

16/995655

Inventors:

- London, GB
Chrisantha Thomas Fernando - London, GB
Alexander Pritzel - London, GB
Dylan Sunil Banarse - Cambridge, GB
Charles Blundell - London, GB
Andrei-Alexandru Rusu - London, GB
Yori Zwols - London, GB
David Ha - San Francisco CA, US

International Classification:

G06N 3/08
G06N 3/04

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.

US Patent:

20190354868, Nov 21, 2019

Filed:

Jul 30, 2019

Appl. No.:

16/526240

Inventors:

- Mountain View CA, US
Chrisantha Thomas Fernando - London, GB
Alexander Pritzel - London, GB
Dylan Sunil Banarse - Cambridge, GB
Charles Blundell - London, GB
Andrei-Alexandru Rusu - London, GB
Yori Zwols - London, GB
David Ha - San Francisco CA, US

International Classification:

G06N 3/08
G06N 3/04

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.