Deepmind Technologies Limited

Matthew Botvinick

Alexander Lerchner

Irina Higgins

Arka Tilak Pal

Demis Hassabis

Loic Matthey-de-l'Endroit

Christopher Paul Burgess

Nicolas Sonnerat

Razvan Pascanu

Charles Blundell

Siddhant Jayakumar

Samuel Ritter

Xiao Jing Wang

Loic Matthey-De-L`ENDROIT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for learning visual concepts using neural networks. One of the methods includes receiving a new symbol input comprising one or more symbols from a vocabulary; and generating a new output image that depicts concepts referred to by the new symbol input, comprising: processing the new symbol input using a symbol encoder neural network to generate a new symbol encoder output for the new symbol input; sampling, from the distribution parameterized by the new symbol encoder output, a respective value for each of a plurality of visual factors; and processing a new image decoder input comprising the respective values for the visual factors using an image decoder neural network to generate the new output image.

Learning visual concepts using neural networks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a system output using a remembered value of a neural network hidden state. In one aspect, a system comprises an external memory that maintains context experience tuples respectively comprising: (i) a key embedding of context data, and (ii) a value of a hidden state of a neural network at the respective previous time step. The neural network is configured to receive a system input and a remembered value of the hidden state of the neural network and to generate a system output. The system comprises a memory interface subsystem that is configured to determine a key embedding for current context data, determine a remembered value of the hidden state of the neural network based on the key embedding, and provide the remembered value of the hidden state as an input to the neural network.

Selecting actions by reverting to previous learned action selection policies

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Philadelphia, PA, United States of America

Matthew Botvinick