论文标题
具有负面证据的多通道神经图形事件模型
A Multi-Channel Neural Graphical Event Model with Negative Evidence
论文作者
论文摘要
事件数据集是在时间表上不规则地发生的各种类型事件的序列，并且它们在众多域中越来越普遍。使用条件强度进行建模事件进行建模的现有工作依赖于使用一些基本参数形式来捕获历史依赖性，或者主要关注诸如预测之类的任务的非参数模型。我们提出了一种非参数深神经网络方法，以估计潜在的强度函数。我们使用一种新型的多渠道RNN，该RNN在每个连续的事件间间隔内引入了假事件时期，从而最佳地加强了无观察事件的负面证据。我们根据模型拟合任务的最新基准评估了我们的方法，该方法是通过日志样式测量的。通过对合成数据集和现实世界数据集的实验，我们发现我们所提出的方法在所研究的大多数数据集上的表现优于现有基准。
Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.
