Recently at CCRi, we have been doing a lot of research in the area of reduced dimensional semantic embedding models: models where semantically similar objects possess similar representations. In a previous post, Nick discussed how such a model can be learned … Read More

In our last post, Nick talked about how n-dimensional (for n small) real-valued representations of relational data can be constructed in a semantically rich way. These fixed length representations can then be fed into more traditional machine learning algorithms, which often require this … Read More