Decay Replay Mining is a deep learning method that utilizes process model notations to predict the next event. However, this method does not intertwine the neural network with the structure of the process model to its full extent. This paper proposes an approach to further interlock the process model of Decay Replay Mining with its neural network for next event prediction. The approach uses a masking layer which is initialized based on the reachability graph of the process model. Additionally, modifications to the neural network architecture are proposed to increase the predictive performance. Experimental results demonstrate the value of the approach and underscore the importance of discovering precise and generalized process models.
Attention Meets Reachability: Structural Equivalence and Efficiency in Grammar-Constrained LLM Decoding
Department of Computer Engineering, Bahçeşehir University, Istanbul, Turkey（计算机工程系，巴切希尔大学，伊斯坦布尔，土耳其）；Department of Industrial Engineering, Bahçeşehir University, Istanbul, Turkey（工业工程系，巴切希尔大学，伊斯坦布尔，土耳其）
AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Anhui University（安徽大学）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）；Zhejiang Lab（之江实验室）
Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
ETH AI Center, ETH Zurich（苏黎世联邦理工学院人工智能中心）；Institute for Dynamic Systems and Control, ETH Zurich（苏黎世联邦理工学院动态系统与控制研究所）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）
