Technology

We explore novel concepts to building learning and reasoning systems.

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Our focus is on problems that demand fast reasoning and effective generalization from small datasets. We believe that the key to modelling complex systems such as those encountered in molecular biology can only be tackled with tools that are able to extract the maximum information and derive rules from sparse and limited data. It requires systems to understand relevancy, similarity, hierarchy and context it more effective ways than achieved by state of the art systems.

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We explore mechanisms such as dynamic representations, unsupervised feature extraction, local-global training and more.