Deepmind Technologies Limited

Google Inc.

Gdm Holding LLC

Yujia Li

Oriol Vinyals

Razvan Pascanu

Nicolas Manfred Otto Heess

Daniel Pieter Wierstra

Peter William Battaglia

Theophane Guillaume Weber

Sebastien Henri Andre Racaniere

Pushmeet Kohli

David Paul Reichert

Lars Buesing

Danilo Jimenez Rezende

Chenjie Gu

Adrià Puigdomènech Badia

Christopher James Dyer

Hanjun Dai

Thomas Dullien

Arthur Clement Guez

Rishabh Singh

Daniel S Tarlow

Adam Anthony Santoro

Victor Constant Bapst

David Nunes Raposo

Matej Balog

Bo Dai

Dale Eric Schuurmans

Adria Puigdomenech Badia

Chenglong Wang

Azade Nazi

Po-Sen Huang

Vinicius Zambaldi

Subhodeep Moitra

Bart Van Merrienboer

Vinod Nair

Sebastien Henri Racaniere

Aditya Paliwal

Miles C Lubin

Danilo Jiminez Rezende

There is described a neural network system for generating a graph, the graph comprising a set of nodes and edges. The system comprises one or more neural networks configured to represent a probability distribution over sequences of node generating decisions and/or edge generating decisions, and one or more computers configured to sample the probability distribution represented by the one or more neural networks to generate a graph.

Graph neural network systems for generating structured representations of objects

There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

Deep neural network system for similarity-based graph representations

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.

Autoregressive graph generation machine learning models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining a graph of nodes and edges that represents an interaction history of the agent with the environment; generating an encoded representation of the graph representing the interaction history of the agent with the environment; processing an input based on the encoded representation of the graph using an action selection neural network, in accordance with current values of action selection neural network parameters, to generate an action selection output; and selecting an action from a plurality of possible actions to be performed by the agent using the action selection output generated by the action selection neural network.

Action selection using interaction history graphs

A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.

Reinforcement learning using a relational network for generating data encoding relationships between entities in an environment

A computer-implemented method for computing node embeddings of a sparse graph that is an input of a sparse graph neural network is described. Each node embedding corresponds to a respective node of the sparse graph and represents feature information of the respective node and a plurality of neighboring nodes of the respective node. The method includes: receiving an adjacency matrix that represents edges of the sparse graph; receiving a weight matrix representing, for each node of the sparse graph, a level of influence of respective neighboring nodes on the node; initializing, for each node of the sparse graph, a respective node embedding; transforming the adjacency matrix into a low-bandwidth adjacency matrix, and performing the following operations at least once: generating a message propagation matrix as a product of the low-bandwidth adjacency matrix, the node embeddings of the nodes, and the weight matrix, wherein the message propagation matrix represents message propagation among the nodes of the sparse graph, and updating the node embeddings of the sparse graph by processing the message propagation matrix and the node embeddings of the nodes using an encoder neural network of the sparse graph neural network.

Optimizing sparse graph neural networks for dense hardware

A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.

Imagination-based agent neural networks

A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.

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London, United Kingdom

Yujia Li