论文标题
编码器 - 模型生成的对手网，用于后缀生成和剩余的业务流程模型预测
Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Prediction of Business Process Models
论文作者
论文摘要
本文提出了一个基于生成对抗网络（GAN）的编码器架构，该体系结构以端到端的方式生成一系列活动及其时间戳。 gan可以与图像等可区分数据（例如图像）合作。但是，后缀是一系列分类项目。为此，我们使用Gumbel-SoftMax分布来获得可区分的连续近似。培训是通过将一个神经网络与另一个玩家游戏中的另一个神经网络相反的作用（因此是“对抗性”的性质），从而导致产生接近地面真相的后缀。从实验评估中可以看出，尽管使用了幼稚的特征编码，并且仅基于控制流量和事件的完成时间，但就预测后缀和相应剩余时间的准确性而言，该方法优于基准。
This paper proposes an encoder-decoder architecture grounded on Generative Adversarial Networks (GANs), that generates a sequence of activities and their timestamps in an end-to-end way. GANs work well with differentiable data such as images. However, a suffix is a sequence of categorical items. To this end, we use the Gumbel-Softmax distribution to get a differentiable continuous approximation. The training works by putting one neural network against the other in a two-player game (hence the "adversarial" nature), which leads to generating suffixes close to the ground truth. From the experimental evaluation it emerges that the approach is superior to the baselines in terms of the accuracy of the predicted suffixes and corresponding remaining times, despite using a naive feature encoding and only engineering features based on control flow and events completion time.
