There are now a number of bidirectional programming languages, where every program can be read both as a forward transformation mapping one data structure to another and as a reve...
J. Nathan Foster, Alexandre Pilkiewicz, Benjamin C...
Abstract. The present paper presents a new approach of how to convert Gold-style [4] learning in the limit into stochastically finite learning with high confidence. We illustrate t...
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
We present here an approach for applying the technique of modeling data transformation manifolds for invariant learning with kernel methods. The approach is based on building a ke...
Although there are a large number of academic and industrial model transformation frameworks available, allowing specification, implementation, maintenance and documentation of mod...
Behzad Bordbar, Gareth Howells, Michael Evans, Ath...