论文标题
随着时间的推移神经数据：通过逻辑规范知情的时间建模
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
论文作者
论文摘要
当可能的事件类型较大时，很难学习如何从过去事件的模式中预测未来事件。训练不受限制的神经模型可能会使虚假模式过高。为了利用特定领域的知识，了解过去事件如何影响事件的当前概率，我们建议使用时间演绎数据库随着时间的流逝跟踪结构化事实。规则有助于证明其他事实和过去事件的事实。每个事实都有一个时间变化的状态 - 一个由神经网计算得出的向量，其拓扑由事实的出处决定，包括其过去事件的经验。任何时间可能的事件类型都是由特殊事实给出的，其概率是与州并肩建模的。在综合和现实世界中，我们表明，从简明的数据核计划中得出的神经概率模型通过在其体系结构中编码适当的域知识来改善预测。
Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large. Training an unrestricted neural model might overfit to spurious patterns. To exploit domain-specific knowledge of how past events might affect an event's present probability, we propose using a temporal deductive database to track structured facts over time. Rules serve to prove facts from other facts and from past events. Each fact has a time-varying state---a vector computed by a neural net whose topology is determined by the fact's provenance, including its experience of past events. The possible event types at any time are given by special facts, whose probabilities are neurally modeled alongside their states. In both synthetic and real-world domains, we show that neural probabilistic models derived from concise Datalog programs improve prediction by encoding appropriate domain knowledge in their architecture.
