正则化神经链路预测器的对抗集
Adversarial Sets for Regularising Neural Link Predictors
摘要：
在对抗训练中，一组模型通过追求竞争目标来共同学习，这些目标通常定义在单个数据实例上。然而，在关系学习和其他非i.i. d领域，目标也可以在实例集上定义。例如，is-a关系的链接预测器需要与传递性属性一致：如果is-a（x_1，x_2）和is-a（x_2，x_3）成立，则is-a（x_1，x_3）也需要成立。在这里，我们使用这样的假设来推导不一致性损失，测量模型在对抗性生成的一组示例上违反假设的程度。训练目标被定义为极大极小问题，其中对手通过最大化不一致损失来找到最具攻击性的对抗性示例，并且通过联合最小化监督损失和对抗性示例上的不一致损失来训练模型。这产生了第一种方法，可以使用无函数的Horn子句（如Datasheet）来正则化任何神经链接预测器，其复杂性与域大小无关。我们发现，对于几个链接预测模型，对手所面临的优化问题有有效的封闭形式的解决方案。在链接预测基准测试上的实验表明，在适当的先验知识下，我们的方法可以显着提高神经链接预测器的所有相关指标。
英文摘要：
In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances. However, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the is-a relation needs to be consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3) hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for deriving an inconsistency loss, measuring the degree to which the model violates the assumptions on an adversarially-generated set of examples. The training objective is defined as a minimax problem, where an adversary finds the most offending adversarial examples by maximising the inconsistency loss, and the model is trained by jointly minimising a supervised loss and the inconsistency loss on the adversarial examples. This yields the first method that can use function-free Horn clauses (as in Datalog) to regularise any neural link predictor, with complexity independent of the domain size. We show that for several link prediction models, the optimisation problem faced by the adversary has efficient closed-form solutions. Experiments on link prediction benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
