Deepmind Technologies Limited

Gdm Holding LLC

Google Inc.

Jack William Rae

Timothy Paul Lillicrap

Razvan Pascanu

Siddhant Jayakumar

Emilio Parisotto

Oriol Vinyals

Maxwell Elliot Jaderberg

Adam Anthony Santoro

Theophane Guillaume Weber

David Nunes Raposo

Ryan Faulkner

Anna Potapenko

Hasuk Song

Sergey Bartunov

Alexander Benjamin Graves

Joao Ferdinando Gomes De Freitas

Gregory Duncan Wayne

Charles Blundell

Ivo Danihelka

Timothy James Alexander Harley

Gabriel Barth-Maron

Simon Osindero

Adrià Puigdomènech Badia

Alexander Pritzel

Trevor Back

Shakir Mohamed

Misha Man Ray Denil

Xavier Glorot

Harry Askham

Joseph R Ledsam

Edward Thomas Grefenstette

Nenad Tomasev

Alexander Novikov

Tom Erez

Jonathan James Hunt

Clemens Ludwig Meyer

Pablo Sprechmann

Mike Chrzanowski

Ivan Protsyuk

Michal Zielinski

Sergio Gomez

Fu-min Wang

Konrad Zolna

Anne Mottram

Jacob Bruce

Caglar Gulcehre

Ashley Deloris Edwards

Andre Saraiva Nobre Dos Santos

Scott Ellison Reed

Suman Ravuri

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent to interact with an environment using an action selection neural network. In one aspect, a method comprises, at each time step in a sequence of time steps: generating a current representation of a state of a task being performed by the agent in the environment as of the current time step as a sequence of data elements; autoregressively generating a sequence of data elements representing a current action to be performed by the agent at the current time step; and after autoregressively generating the sequence of data elements representing the current action, causing the agent to perform the current action at the current time step.

Autoregressively generating sequences of data elements defining actions to be performed by an agent

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input that is a sequence to generate a network output. In one aspect, one of the methods includes, for each particular sequence of layer inputs: for each attention layer in the neural network: maintaining episodic memory data; maintaining compressed memory data; receiving a layer input to be processed by the attention layer; and applying an attention mechanism over (i) the compressed representation in the compressed memory data for the layer, (ii) the hidden states in the episodic memory data for the layer, and (iii) the respective hidden state at each of the plurality of input positions in the particular network input to generate a respective activation for each input position in the layer input.

Augmenting attention-based neural networks to selectively attend to past inputs

A system including an attention neural network that is configured to receive an input sequence and to process the input sequence to generate an output is described. The attention neural network includes: an attention block configured to receive a query input, a key input, and a value input that are derived from an attention block input. The attention block includes an attention neural network layer configured to: receive an attention layer input derived from the query input, the key input, and the value input, and apply an attention mechanism to the query input, the key input, and the value input to generate an attention layer output for the attention neural network layer; and a gating neural network layer configured to apply a gating mechanism to the attention block input and the attention layer output of the attention neural network layer to generate a gated attention output.

Gated attention neural networks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing associative memory. In one aspect a system comprises an associative memory neural network to process an input to generate an output that defines an energy corresponding to the input. A reading subsystem retrieves stored information from the associative memory neural network. The reading subsystem performs operations including receiving a given, i.e. query, input and retrieving a data element from the associative memory neural network that is associated with the given input. The retrieving is performed by iteratively adjusting the given input using the associative memory neural network.

Energy-based associative memory neural networks

There is described herein a computer-implemented method of processing an input data item. The method comprises processing the input data item using a parametric model to generate output data, wherein the parametric model comprises a first sub-model and a second sub-model. The processing comprises processing, by the first sub-model, the input data to generate a query data item, retrieving, from a memory storing data point-value pairs, at least one data point-value pair based upon the query data item and modifying weights of the second sub-model based upon the retrieved at least one data point-value pair. The output data is then generated based upon the modified second sub-model.

Machine learning systems with memory based parameter adaptation for learning fast and slower

A system for compressed data storage using a neural network. The system comprises a memory comprising a plurality of memory locations configured to store data; a query neural network configured to process a representation of an input data item to generate a query; an immutable key data store comprising key data for indexing the plurality of memory locations; an addressing system configured to process the key data and the query to generate a weighting associated with the plurality of memory locations; a memory read system configured to generate output memory data from the memory based upon the generated weighting associated with the plurality of memory locations and the data stored at the plurality of memory locations; and a memory write system configured to write received write data to the memory based upon the generated weighting associated with the plurality of memory locations.

Scalable and compressive neural network data storage system

A system including one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement a memory and memory-based neural network is described. The memory is configured to store a respective memory vector at each of a plurality of memory locations in the memory. The memory-based neural network is configured to: at each of a plurality of time steps: receive an input; determine an update to the memory, wherein determining the update comprising applying an attention mechanism over the memory vectors in the memory and the received input; update the memory using the determined update to the memory; and generate an output for the current time step using the updated memory.

Neural networks with relational memory

Augmenting attentioned-based neural networks to selectively attend to past inputs

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks. One of the methods includes receiving electronic health record data for a patient; generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time window time steps, comprising, for each time window time step: determining, for each of the possible numerical features, whether the numerical feature occurred during the time window; and generating, for each of the possible numerical features, one or more presence features that identify whether the numerical feature occurred during the time window; and processing the input sequence using a neural network to generate a neural network output that characterizes a predicted likelihood that an adverse health event will occur to the patient.

Prediction of future adverse health events using neural networks by pre-processing input sequences to include presence features

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory. One of the systems includes a sparse memory access subsystem that is configured to perform operations comprising generating a sparse set of reading weights that includes a respective reading weight for each of the plurality of locations in the external memory using the read key, reading data from the plurality of locations in the external memory in accordance with the sparse set of reading weights, generating a set of writing weights that includes a respective writing weight for each of the plurality of locations in the external memory, and writing the write vector to the plurality of locations in the external memory in accordance with the writing weights.

Augmenting neural networks with sparsely-accessed external memory

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London, United Kingdom

Jack William Rae