We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. This is done by including reasoning layers that implement finite-domain quantification over objects and time. The structure allows them to generalize directly to input instances with varying numbers of objects in temporal sequences of varying lengths. We evaluate TOQ-Nets on input domains that require recognizing event-types in terms of complex temporal relational patterns. We demonstrate that TOQ-Nets can generalize from small amounts of data to scenarios containing more objects than were present during training and to temporal warpings of input sequences.

中文翻译：

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时间和对象量化网络

我们提出了时间和对象量化网络 (TOQ-Nets)，这是一类具有结构偏差的新型神经符号网络，使它们能够学习识别复杂的时间关系事件。这是通过包含在对象和时间上实现有限域量化的推理层来完成的。该结构允许它们直接泛化到具有不同长度的时间序列中不同数量的对象的输入实例。我们在需要根据复杂的时间关系模式识别事件类型的输入域上评估 TOQ-Nets。我们证明了 TOQ-Nets 可以从少量数据泛化到包含比训练期间存在的对象更多的场景以及输入序列的时间扭曲。