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Non-Parametric Learning of Lifted Restricted Boltzmann Machines
We consider the problem of discriminatively learning restricted Boltzmann
machines in the presence of relational data. Unlike previous approaches that
employ a rule learner (for structure learning) and a weight learner (for
parameter learning) sequentially, we develop a gradient-boosted approach that
performs both simultaneously. Our approach learns a set of weak relational
regression trees, whose paths from root to leaf are conjunctive clauses and
represent the structure, and whose leaf values represent the parameters. When
the learned relational regression trees are transformed into a lifted RBM, its
hidden nodes are precisely the conjunctive clauses derived from the relational
regression trees. This leads to a more interpretable and explainable model. Our
empirical evaluations clearly demonstrate this aspect, while displaying no loss
in effectiveness of the learned models.Comment: 33 pages, 12 figure