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Relational Connectionist Models bring together the complementary strengths of scalability and interpretability. We have considered learning Boltzmann machines for relational data; specifically generating features from lifted random walks that form the observed features of Boltzmann machines and produce efficient models when tested on six relational datasets.

Furthermore, we look at lifted relational connectionist models learned with the help of parameter tying and combining rules. This work is ongoing.


  1. Navdeep Kaur, Gautam Kunapuli, Tushar Khot, William Cohen, Kristian Kersting and Sriraam Natarajan, “Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach”, ILP 2017.