论文标题
在神经网络中注入域知识：关于约束问题的受控实验
Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
论文作者
论文摘要
给定足够的数据，深度神经网络（DNN）能够以高精度学习复杂的输入关系。但是，在几个领域中，数据稀缺或昂贵，而可以提供大量的专家知识。如果我们可以在DNN中注入这些其他信息，这似乎是合理的，我们可以缓解学习过程。一种情况是构成问题的情况，对此声明性方法的存在，纯ML解决方案获得了不同的成功。使用经典的约束问题作为案例研究，我们执行受控的实验，以探测DNN中逐步添加域和经验知识的影响。我们的结果非常令人鼓舞，表明（至少在我们的设置中）在训练时间嵌入域知识可能会产生相当大的效果，并且少量的经验知识足以获得实际上有用的结果。
Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy. In several domains, however, data is scarce or expensive to retrieve, while a substantial amount of expert knowledge is available. It seems reasonable that if we can inject this additional information in the DNN, we could ease the learning process. One such case is that of Constraint Problems, for which declarative approaches exists and pure ML solutions have obtained mixed success. Using a classical constrained problem as a case study, we perform controlled experiments to probe the impact of progressively adding domain and empirical knowledge in the DNN. Our results are very encouraging, showing that (at least in our setup) embedding domain knowledge at training time can have a considerable effect and that a small amount of empirical knowledge is sufficient to obtain practically useful results.
