A new model for generating survival trajectories and data based on applying
an autoencoder of a specific structure is proposed. It solves three tasks.
First, it provides predictions in the form of the expected event time and the
survival function for a new generated feature vector on the basis of the Beran
estimator. Second, the model generates additional data based on a given
training set that would supplement the original dataset. Third, the most
important, it generates a prototype time-dependent trajectory for an object,
which characterizes how features of the object could be changed to achieve a
different time to an event. The trajectory can be viewed as a type of the
counterfactual explanation. The proposed model is robust during training and
inference due to a specific weighting scheme incorporating into the variational
autoencoder. The model also determines the censored indicators of new generated
data by solving a classification task. The paper demonstrates the efficiency
and properties of the proposed model using numerical experiments on synthetic
and real datasets. The code of the algorithm implementing the proposed model is
publicly available.

提出了一种基于特定结构的自动编码器生成生存轨迹和数据的新模型，解决了预测事件时间和生存函数、生成附加数据和生成原型时间依赖轨迹等三个任务，其鲁棒性和性能经通过合成和真实数据实验证明。

生成可解释的生存轨迹和数据

Generating Survival Interpretable Trajectories and Data

How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)?
TKGs represent facts about entities and their relations, where each fact is
associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts
from time-evolving KGs, is crucial for many applications to provide intelligent
services. However, despite the prevalence of real-world data that can be
represented as TKGs, most methods focus on reasoning over static knowledge
graphs, or cannot predict future events. In this paper, we present a problem
formulation that unifies the two major problems that need to be addressed for
an effective reasoning over TKGs, namely, modeling the event time and the
evolving network structure. Our proposed method EvoKG jointly models both tasks
in an effective framework, which captures the ever-changing structural and
temporal dynamics in TKGs via recurrent event modeling, and models the
interactions between entities based on the temporal neighborhood aggregation
framework. Further, EvoKG achieves an accurate modeling of event time, using
flexible and efficient mechanisms based on neural density estimation.
Experiments show that EvoKG outperforms existing methods in terms of
effectiveness (up to 77% and 116% more accurate time and link prediction) and
efficiency.

本研究提出了一种新的方法 EvoKG，能够联合建模事件时间和演化网络结构，采用循环事件建模和基于时间邻域聚合框架模拟 TKG 中的结构和时态动态，使用神经密度估计实现事件时间的准确建模，并通过实验证明了其效果和效率均比现有方法更好。