论文标题
Hypro：用于事件序列的长马预测的杂交概率模型
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
论文作者
论文摘要
在本文中，我们解决了对事件序列进行长途预测的重要但不足的问题。由于其自回归结构，现有的最新模型在此任务上表现不佳。我们提出了一种自然符合此任务的杂种概率模型Harpro：其第一部分是一种自回旋基础模型，该模型学会了提出预测；它的第二部分是一种能量函数，该函数学会重新持续提出的建议，以使更现实的预测最终以更高的概率出现。我们还为此模型提出了有效的培训和推理算法。在多个现实世界数据集上的实验表明，我们提出的Hypro模型可以显着优于以前的模型，以对未来事件进行长远预测。我们还进行了一系列消融研究，以研究我们提出方法的每个组成部分的有效性。
In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences. Existing state-of-the-art models do not perform well at this task due to their autoregressive structure. We propose HYPRO, a hybridly normalized probabilistic model that naturally fits this task: its first part is an autoregressive base model that learns to propose predictions; its second part is an energy function that learns to reweight the proposals such that more realistic predictions end up with higher probabilities. We also propose efficient training and inference algorithms for this model. Experiments on multiple real-world datasets demonstrate that our proposed HYPRO model can significantly outperform previous models at making long-horizon predictions of future events. We also conduct a range of ablation studies to investigate the effectiveness of each component of our proposed methods.
