Babylon Partners Limited

Dane Grant Sherburn

Nils Yannick Hammerla

Pietro Cavallo

Daniel William Busbridge

Vitalii Zhelezniak

Francisco Vargas

Kamen Brestnichki

A graphical classification method for classifying graphical structures, said graphical structures comprising nodes defined by feature vectors and having relations between the nodes. The method includes representing the feature vectors and relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation said third combined representation being an indication of the classification of the graphical structure.

Method and system for extracting information from graphs

A computer implemented method for classifying molecular structures is provided. The method includes representing the elements and atoms in a molecular structure as nodes and the bonds as relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation. The method also includes mapping said third combined representation to a feature vector indicating properties of the molecular structure.

A method of training a model, said model being adapted to map a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes, wherein the mapping comprising using a projection kernel to map the nodes of the first graphical structure to nodes of an intermediate representation and using an attention kernel to enact the attention mechanism. The method includes receiving a training data set comprising an output layer and a corresponding input layer. The method also includes training the parameters of the projection kernel and the attention kernel using the training data set.

A method of mapping a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes.

A computer-implemented method comprising: receiving the first set of words and the second set of words, wherein each of the first and second sets of words; calculating a first likelihood-based measure representing how well a first model can be fit to the first and second sets of words, the first model comprising a shared parametric distribution representing both the first and second sets of words; calculating a second likelihood-based measure representing how well a second model can be fit to the first and second sets of words, the second model comprising a first parametric distribution representing the first set of words and a second parametric distribution representing the second set of words; calculating a similarity score based on a ratio of the first likelihood measure to the second likelihood measure, the similarity score being representative of the similarity between the first and second sets of words; and outputting the similarity score.

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London, United Kingdom

Dane Grant Sherburn