论文标题
带有三角图的快速，灵活的时间点过程
Fast and Flexible Temporal Point Processes with Triangular Maps
论文作者
论文摘要
时间点过程（TPP）模型与复发性神经网络相结合，为建模连续时间事件数据提供了强大的框架。尽管此类模型是灵活的，但它们本质上是顺序的，因此无法从现代硬件的并行性中受益。通过利用归一化流的最新发展，我们设计了Tritpp，这是一种新的非透明TPP模型，在该模型中，采样和可能性计算都可以并行进行。 TRITPP匹配基于RNN的方法的灵活性，但允许更快地采样阶。这使我们能够使用新模型在连续的离散状态系统中进行变异推断。我们证明了拟议框架在合成和现实世界数据集上的优势。
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the recent developments in the field of normalizing flows, we design TriTPP -- a new class of non-recurrent TPP models, where both sampling and likelihood computation can be done in parallel. TriTPP matches the flexibility of RNN-based methods but permits orders of magnitude faster sampling. This enables us to use the new model for variational inference in continuous-time discrete-state systems. We demonstrate the advantages of the proposed framework on synthetic and real-world datasets.
