Machine learning models are routinely integrated into process mining
pipelines to carry out tasks like data transformation, noise reduction, anomaly
detection, classification, and prediction. Often, the design of such models is
based on some ad-hoc assumptions about the corresponding data distributions,
which are not necessarily in accordance with the non-parametric distributions
typically observed with process data. Moreover, the learning procedure they
follow ignores the constraints concurrency imposes to process data. Data
encoding is a key element to smooth the mismatch between these assumptions but
its potential is poorly exploited. In this paper, we argue that a deeper
insight into the issues raised by training machine learning models with process
data is crucial to ground a sound integration of process mining and machine
learning. Our analysis of such issues is aimed at laying the foundation for a
methodology aimed at correctly aligning machine learning with process mining
requirements and stimulating the research to elaborate in this direction.

本文探讨了在将机器学习模型整合进过程挖掘管道中所需考虑的问题，特别是数据分布的非参数性以及并发限制，提出了正确地将机器学习与过程挖掘相结合的方法学基础及在这一方向上的研究思路。

为流程挖掘定制机器学习

Tailoring Machine Learning for Process Mining

Single image pose estimation is a fundamental problem in many vision and
robotics tasks, and existing deep learning approaches suffer by not completely
modeling and handling: i) uncertainty about the predictions, and ii) symmetric
objects with multiple (sometimes infinite) correct poses. To this end, we
introduce a method to estimate arbitrary, non-parametric distributions on
SO(3). Our key idea is to represent the distributions implicitly, with a neural
network that estimates the probability given the input image and a candidate
pose. Grid sampling or gradient ascent can be used to find the most likely
pose, but it is also possible to evaluate the probability at any pose, enabling
reasoning about symmetries and uncertainty. This is the most general way of
representing distributions on manifolds, and to showcase the rich expressive
power, we introduce a dataset of challenging symmetric and nearly-symmetric
objects. We require no supervision on pose uncertainty -- the model trains only
with a single pose per example. Nonetheless, our implicit model is highly
expressive to handle complex distributions over 3D poses, while still obtaining
accurate pose estimation on standard non-ambiguous environments, achieving
state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks.

本研究提出了一种基于非参数分布的单幅图像姿态估计方法，该方法使用神经网络隐式地表示任意姿态的概率分布，并考虑了不确定性和多重对称问题。该方法在处理三维姿态复杂分布时表现优异。