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.

中文翻译：

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通过时间的神经数据记录：通过逻辑规范的知情时间建模

当可能的事件类型集很大时，学习如何根据过去事件的模式预测未来事件是很困难的。训练不受限制的神经模型可能会过度拟合虚假模式。为了利用过去事件如何影响事件当前概率的特定领域知识，我们建议使用时间演绎数据库来跟踪结构化事实随着时间的推移。规则用于从其他事实和过去事件中证明事实。每个事实都有一个随时间变化的状态——一个由神经网络计算的向量，其拓扑结构由事实的来源决定，包括它对过去事件的经验。任何时候可能的事件类型由特殊事实给出，其概率与其状态一起被神经建模。在合成领域和现实领域中，