Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose higher level composite events. Examples of a composite event are a patient's medical symptom or a baseball player hitting a home run, caused distinct temporal orderings of patient vitals and player movements respectively. Such salient composite events are provided as labels in temporal datasets and most works optimize models to predict these composite event labels directly. We focus on uncovering the underlying atomic events and their relations that lead to the composite events within a noisy temporal data setting. We propose Neural Temporal Logic Programming (Neural TLP) which first learns implicit temporal relations between atomic events and then lifts logic rules for composite events, given only the composite events labels for supervision. This is done through efficiently searching through the combinatorial space of all temporal logic rules in an end-to-end differentiable manner. We evaluate our method on video and healthcare datasets where it outperforms the baseline methods for rule discovery.
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
Dept. of Mechanical and Aerospace Engineering, George Washington University, USA（机械与航空航天工程系，乔治华盛顿大学）；Dept. of Mechanical Engineering Sciences, University of Surrey, UK（机械工程科学系，萨里大学）
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
Tsinghua University（清华大学）；University of Southern California（南加利福尼亚大学）
可解释性和有效性是构建推荐系统的两个关键方面。以前的努力主要集中在合并边信息，以实现更好的推荐性能。然而，这些方法具有一些弱点：（1）基于神经网络的嵌入方法的预测难以解释和调试;（2）基于符号、图形的方法（例如，基于Meta路径的模型）需要人工努力和领域知识来定义模式和规则，并且忽略项目关联类型（例如，可替换的和互补的）。在本文中，我们提出了一种新的联合学习框架集成\textit{induction of explainable rules from knowledge graph}与\textit{construction of a rule-guided neural recommendation model}。该框架鼓励两个模块相互补充，以产生有效的和可解释的推荐：1）归纳规则，从以项目为中心的知识图谱中挖掘，总结常见的多跳关系模式，用于推断不同的项目关联，并为模型预测提供人类可读的解释; 2）推荐模块可以通过归纳规则进行扩充，从而在处理冷启动问题时具有更好的泛化能力。大量的实验\footnote{Code and data can be found at: \url{https://github.com/THUIR/RuleRec}}表明，我们提出的方法已经取得了显着的改善，在现实世界的数据集上的基线项目推荐。我们的模型在通过将项目名称链接到相关实体而生成的“嘈杂”项目知识…
Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Big Data and Brain Computing（北京大数据与脑计算高精尖创新中心）；China’s Aviation System Engineering Research Institute（中国航空系统工程研究所）
Learning with Dual-level Noisy Correspondence for Multi-modal Entity Alignment
College of Computer Science, Sichuan University（四川大学计算机学院）；National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University（四川省工程数值模拟基础算法与模型国家重点实验室）
Online Multi-Label Classification under Noisy and Changing Label Distribution
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；School of Computing, National University of Singapore（新加坡国立大学计算学院）
Data-Driven Discovery of Emergent Dynamics in Reaction-Diffusion Systems from Sparse and Noisy Observations
Department of ICT and Natural Sciences, Norwegian University of Science and Technology（信息与自然科学系，挪威科学技术大学）；Department of Computer Science, Norwegian University of Science and Technology（计算机科学系，挪威科学技术大学）；Artificial Intelligence Group, Wageningen University and Research（人工智能组，瓦赫宁根大学与研究中心）；Department of Mechanical Engineering and Technology Management, Norwegian University of Life Sciences（机械工程与技术管理系，挪威生命科学大学）
Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error
Principles of Informatics Research Division, National Institute of Informatics (NII)（国立信息学研究所信息学原理研究部）；Informatics Program, Graduate Institute for Advanced Studies (SOKENDAI)（综合研究大学院大学信息学项目）
