Keywords: vector space embeddings, statistical relational learning, neural networks, explainable AI Duration: 24 months Start date: 1 January 2018 (or as soon as possible thereafter) Closing date: 2 November 2017
Applications are invited for two postdoctoral research posts at Cardiff University’s School of Computer Science & Informatics in the context of Steven Schockaert's FLEXILOG project, which is funded by the European Research Council (ERC). The overall aims of this project are (i) to learn interpretable vector space representations of entities and their relationships, and (ii) to exploit these vector space representations for various forms of flexible reasoning with, and learning from structured data. More information about FLEXILOG can be found on the project website: http://www.cs.cf.ac.uk/flexilog/ The aim of these positions will be to contribute to one or more of the following topics. 1) Learning structured event embeddings. In contrast to existing approaches, the learned embeddings will explicitly model which entities participate in the events, how they are related, and how their relationships are affected by different events. This will require combining ideas from neural network models for event embedding (e.g. based on LSTMs) with cognitively inspired representations (e.g. based on the theory of conceptual spaces). Among others, the resulting model will allow us to uncover more intricate causal relationships, to generate supporting explanations for causal predictions, to incorporate prior knowledge, and to transfer learned knowledge between domains. 2) Combining statistical relational learning with vector space models of commonsense reasoning. Low-dimensional vector space representations can be used to identify plausible formulas that are missing from a given knowledge base, intuitively by applying a kind of similarity or analogy based reasoning. Statistical relational learning (SRL) can also be used to infer plausible formulas, but instead relies on modelling statistical dependencies among relational facts at the symbolic level. Unifying both methodologies will allow us to develop powerful inference methods that combine their complementary strengths, enabling interpretable and robust plausible reasoning from sparse relational data. 3) Geometric representations of logical theories. Most vector space models for knowledge base completion simply represent entities, attributes and relations as vectors. In many domains, however, plausible inferences rely on complex dependencies that cannot be captured by such representations. As an alternative, we will develop methods in which predicates are represented as regions, and logical formulas correspond to qualitative constraints on the spatial configurations of these regions. This model will support more complex inferences than existing approaches, will allow us to exploit existing domain knowledge when learning vector space representations, and will conversely allow us derive approximate logical theories from a learned embedding. Cardiff University is a member of the Russell Group of research universities, and was ranked 5th in the UK based on the quality of research in the 2014 Research Evaluation Framework. The university has a successful School of Computer Science & Informatics with an international reputation for its teaching and research activities. Cardiff is a strong and vibrant capital city with good transportation links and an excellent range of housing available. For more details about the positions, please contact Steven Schockaert ([email protected]<mailto:[email protected]>). For instructions on how to apply, please go to www.cardiff.ac.uk/jobs<http://www.cardiff.ac.uk/jobs> and search for job 6522BR. Please note the requirement to evidence all essential criteria in the supporting statement.
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