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.

中文翻译：

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提升受限玻尔兹曼机的非参数学习

我们考虑在存在关系数据的情况下有区别地学习受限玻尔兹曼机的问题。与之前依次采用规则学习器（用于结构学习）和权重学习器（用于参数学习）的方法不同，我们开发了一种同时执行这两种方法的梯度提升方法。我们的方法学习一组弱关系回归树，其从根到叶的路径是连接子句并表示结构，其叶值表示参数。当学习到的关系回归树被转化为提升的 RBM 时，它的隐藏节点正是从关系回归树中导出的连接子句。这导致了一个更具可解释性和可解释性的模型。我们的实证评估清楚地证明了这一方面，